WOS (ISI) Journals

Network reliability standards should ideally be informed by quantitative probabilistic models that quantify the costs and benefits associated with different levels of supply-quality improvement. However, these probabilistic models, such as those used to determine transmission network investments, require component-level outage and repair rates that are frequently missing or incomplete. To address this limitation, this paper proposes a two-part framework that first infers these missing input parameters and then delivers high-quality assessments of the investment costs required to attain a certain supply-quality level. In the first part, an Inverse Probabilistic Security-Constrained Optimal Power Flow (Inverse PSC-OPF) is developed to infer network-component reliability parameters—specifically outage and repair rates—directly from observed system-level continuity metrics routinely documented by regulatory authorities, namely interruption duration, interruption frequency, and unserved energy. In the second part, these calibrated parameters are integrated into a probabilistic transmission expansion planning model to find economically justifiable supply-quality improvements. We apply the framework to the Chilean transmission system (≈ 2,200 lines/transformers, ≈ 2,400 buses), where the resulting optimal investments, together with their costs and benefits across reliability levels, provided insights that informed the formulation of Chile’s national transmission reliability standard.

Resilience planning is crucial for sustainable energy transitions, particularly in mitigating the impacts of extreme weather events and natural hazards. This study examines the integration of hydrogen as a new energy carrier, focusing on its technical flexibility and potential stresses. We connect a resilience framework comprising four phases—hazard characterization, system component vulnerability assessment, power system response, and recovery—to an energy system optimization model. This tool plans the capacity expansion with economic dispatch under contingency scenarios to calculate resilience metrics, including energy not served and generation availability. To demonstrate the methodology, a case study on New Zealand compares the effects of three hydrogen spatial distribution scenarios: concentrated, intermediate, and distributed. Results indicate that a distributed hydrogen scenario enhances resilience by reducing unserved electricity and unmet hydrogen demand while achieving the lowest total system costs. These findings support adopting a nationwide distributed hydrogen hub strategy to improve system adaptability. The proposed framework is scalable and adaptable to other countries, providing a robust tool for integrating resilience into energy planning.

Amid escalating risks of energy shortages driven by climate change and sociopolitical instability, effective risk communication strategies are essential for fostering public cooperation in energy-saving initiatives. This study investigates how the design of energy risk communication campaigns can influence public response. Drawing on a behavioral decision framework, we conducted large-scale, randomized online experiments in the UK (N = 3,836) and Chile (N = 2,607) during periods of potential energy shortages. We tested four key factors influencing the impact of energy risk communication campaigns: the framing of the crisis’s cause (local vs. global), the credibility and role of the message source, the emphasis on specific energy-saving actions, and the underlying motivations driving household actions (economic, environmental, or social). Our findings indicate that messages emphasizing locally controllable causes, such as domestic energy policies, can enhance willingness to save energy. In the UK, which is geographically closer to the Ukraine–Russia war, framing the cause of the crisis around the conflict also proves particularly effective. Government-sourced messages that highlight specific industry efforts to mitigate the crisis improve public perceptions of institutions. Campaigns focusing on high-impact energy-saving recommendations are more effective in encouraging commitment to saving energy than a mixed approach. Prosocial messages that frame energy saving as preventing blackouts increase risk awareness but do not outperform messages framed around saving money in terms of energy-saving intentions. These insights underscore the need for targeted, contextually relevant risk communication strategies and offer policymakers valuable guidance for improving public response and engagement in preparation for energy crises.

Prosumers’ deployment of distributed energy resources (DER) is shaped by market incentives and budget constraints. Effective DER integration can reduce the long-term need for distribution network infrastructure, thereby diminishing overall electricity costs. Nevertheless, it may negatively impact cost allocation among users from different socioeconomic backgrounds. To analyze the efficiency and distributional aspects of DER deployment across different socioeconomic groups, this study employs a novel equilibrium model based on Stackelberg games. The model simulates interactions between a proactive low-voltage distribution network planner and prosumers who may invest in photovoltaic systems and batteries. Prosumers aim to minimize expenses based on tariffs, without knowledge of their peers’ decisions. Due to the non-linearities introduced by the tariff structure, a Gauss-Seidel algorithm is employed to reduce model complexity. The study examines six tariff combinations, revealing that cost allocation varies significantly depending on the tariff design. In this regard, the results show the risks associated with certain tariff designs, including potential losses in efficiency or unfair cost allocation. Likewise, the results highlight the value of cost-reflective tariffs in reducing unintended cross-subsidies. These findings underscore the importance of thoughtful tariff design and network planning in promoting both a distributional fairness DER deployment and system-wide efficiency.

In the shift towards decarbonization, it’s crucial to understand how different transmission cost allocation schemes affect investment incentives. In practice, these schemes aim not just for economic efficiency but also fairness, and, in the decarbonization context, these can also support policy objectives that aim at renewable integration and reductions in CO2 emissions. This study introduces a market equilibrium model and solution approach that can be used to analyze how different network tariff schemes influence investments in generation, energy storage, and transmission. Assuming a competitive market, the model quantifies the long-term effects of network tariffs on investment decisions through an iterative Gauss–Seidel process, achieving market equilibrium. This process ensures transmission costs are fully covered by congestion rents and network tariffs and that generators recover all costs through energy sales. Evaluating outcomes under five network tariff schemes through illustrative and real-world case studies, our findings show the varying impacts of network tariffs on investments in both thermal and low-carbon power systems, and their implications for cost efficiency and CO2 emissions.

This paper studies the relationship between the operational demand-side flexibility provided by the centralised coordination of distributed energy resources (DER) and the mitigation of economic risks in long-term transmission planning. DER coordination could represent an alternative to utility-scale network investments, offering increased operational flexibility, faster deployment, and fewer social licence issues. However, significant uncertainty surrounds DER uptake due to regulation and challenging consumer engagement. A multi-stage Conditional Value-at-Risk (CVaR) scenario-based formulation is introduced to study the techno-economic value of DER coordination across uncertain scenarios and planners’ risk aversion preferences. Using a multi-stage decision tree, we endogenously incorporate long-term planning uncertainties, including DER and renewable energy uptake, retirement of coal generation, fuel and capital costs, as well as lead times. Case studies using real scenarios from Australia’s National Electricity Market (NEM) reveal that DER coordination could reduce expected total system costs by up to 11 % and CVaR by 14 %. Coordination could also lead to narrower investment portfolios across several scenarios, with transmission capacity reductions of up to 4 GW in later stages. In contrast, a system lacking DER coordination faces poorer economic performance and a heightened risk of committing to inefficient investment paths, with reliance on high-capital investments increasing by up to 50 % of installed capacity.

Transmission line routing plays a crucial role in the planning and development of electricity transmission infrastructure. Traditional approaches to route selection often concentrate on finding a single route, potentially overlooking optimal solutions and not fully capturing the complexities and trade-offs inherent in the process. In this paper, we propose a novel methodology for generating optimal alternatives for transmission line routes, designed to provide realistic options for decision-makers in real-life transmission planning studies. Our approach integrates advanced optimization techniques with geographic information systems (GIS) and multi-criteria decision analysis (MCDA) to systematically explore and evaluate route alternatives based on technical, environmental, social, and economic criteria. By generating a diverse set of realistic routes, the methodology enables decision-makers to make informed choices that balance competing objectives and the interests of different stakeholders. Through a case study in Chile, we demonstrate the ability of the proposed methodology to produce route alternatives that represent various preferences across techno-economic and socio-environmental factors. By offering a comprehensive set of options, this methodology supports stakeholders in selecting routes that fulfill infrastructure needs while addressing economic, societal and environmental concerns.

Power networks have traditionally been designed to withstand credible outages, such as N-1 or N-2, often overlooking the complex interdependencies that can lead to cascading outages in modern systems. To address these evolving risks, planning models must adapt to better anticipate and mitigate cascading outages, while also guiding network expansion and the integration of advanced technologies. This paper presents a methodological framework for optimizing network expansion and battery storage investments to mitigate cascading outages. The framework uses an Optimization via Simulation (OvS) approach that incorporates detailed system failure simulations to assess the effectiveness of various network enhancements, including transmission lines, power transformers, reactive power compensation devices and battery storage units. In addition, a sampling process is used to select the number of trigger outage scenarios to be considered within the OvS approach. The effectiveness of the proposed framework is demonstrated on two test networks: a modified version of the IEEE 24-bus test network and the German transmission network. The main findings demonstrate: (a) that incorporating cascading outages into investment planning leads to different network enhancement decisions compared to conventional planning models (which exclude cascading outages), (b) that an optimal mix of network enhancements can significantly mitigate cascading outages, and (c) the computational scalability of the proposed framework.

This paper studies the impact of distributed energy resources (DER) on economic displacements or delays of power system investments. We investigate how the operational flexibility from DER controllability can influence the integrated expansion of transmission and energy storage. Given the interplay of flexibility provision from different technologies, accurately representing uncertainties is essential to avoid over- or under-estimating the flexible operational capabilities of DER. To address this challenge, we propose a multi-stage stochastic expansion planning model that can optimise transmission and storage investments, as well as DER services against long-term uncertainties and detailed operational constraints. We employ a four-stage scenario tree to represent uncertainties and a Dantzig–Wolfe decomposition within a column generation approach to tackle computational challenges. Case studies performed on real Australian National Electricity Market (NEM) scenarios demonstrate that a deterministic model overestimates the capabilities of controllable DER to displace transmission investments, particularly in early stages. Conversely, the proposed stochastic model provides a more measured assessment, maintaining a steadier estimate of transmission displacement potential by controllable DER throughout various stages.

The increased penetration of renewables through converter interfaced generation (CIG) and the associated displacement of synchronous generators (SGs) can significantly reduce system strength. Although some network enhancements, such as the installation of battery energy storage systems (BESS), can partially counterbalance the reduction in strength, current network planning methodologies overlook system strength effects. In this context, this work proposes a new optimization model for co-optimizing investments in network expansions and battery systems to meet, at minimum cost, system strength requirements quantified through short circuit levels (SCL). Recognizing that SCL needs vary on a case-by-case basis, this model incorporates a logistic regression analysis trained on offline time-domain dynamic simulations; this approach effectively correlates SCL with system stability, providing a tailored strategy for each power system. To solve the optimization, we also propose an algorithm to iteratively find the optimal solution for the co-optimized portfolio of new transmission and energy storage investments. Through various case studies, we demonstrate the importance of considering system strength requirements in planning studies with significant penetration of CIG.

Critical component outages can lead to widespread cascading propagation, which is however typically ignored in existing investment planning approaches. To address this gap, this paper seamlessly integrates advanced cascading failure analysis into resilient investment planning. It first deploys a stochastic simulator to generate spatiotemporal high-impact low-probability (HILP) events, which are then assessed using a cascading failure model, generating various cascading quantification metrics (CQMs). The framework explicitly quantifies tail risks (i.e., HILP events) using Conditional Value-at-Risk (CVaR) with a confidence level determined by unsupervised clustering, instead of using a predetermined confidence level. This enables the more tailored identification of a set of worst-case scenarios for the system under investigation, improving its practicality. An optimization model then utilizes the outputs of the cascading analysis and the defined CVaR confidence level to identify investment portfolios that provide a hedge against cascading failures. The proposed work is demonstrated on the IEEE 39-bus system, revealing reduced cascading propagation.

The reduction in system inertia is creating an important market for frequency-containment Ancillary Services (AS) such as enhanced frequency response (e.g., provided by battery storage), traditional primary frequency response and inertia itself. This market presents an important difference with the energy-only market: while the need for energy production is driven by the demand from consumers, frequency-containment AS are procured because of the need to deal with the largest generation/demand loss in the system (or smaller losses that could potentially compromise frequency stability). Thus, a question that arises is: who should pay for frequency-containment AS? In this work, we propose a cost-allocation methodology based on the nucleolus concept, in order to distribute the total payments for frequency-containment AS among all generators or loads that create the need for these services. It is shown that this method complies with necessary properties for the AS market, such as avoidance of cross-subsidies and maintaining players in this cooperative game. Finally, we demonstrate its practical applicability through a case study for the Great Britain power system, while comparing its performance with two alternative mechanisms, namely proportional and Shapley value cost allocation.

The extensive integration of renewable generation in electricity systems is significantly increasing the variability and correlation in power availability and the need for energy storage capacity. This increased uncertainty and storage capacity should be considered in operational decisions such as the short-term unit commitment (UC) problem. In this work, we formulate a day-ahead UC problem with energy storage, considering multistage correlated uncertainty on renewables’ power availability. We solve this multistage stochastic unit commitment (MSUC) problem with integer variables in the first stage using a new variant of SDDP that can explicitly deal with temporal correlations. Our computational results on the IEEE 118-bus system demonstrate the significance of considering multistage uncertainty and correlations, comparing our solution with other multistage solutions, two-stage solutions, and deterministic solutions typically used by industry. We also solve the MSUC problem for a representation of the Chilean power system, finding superior UC solutions for scenarios where adapting generation to the unfolding uncertainty is costly. Finally, we demonstrate that the MSUC approach can be used to define a more efficient deterministic UC solution, outperforming the current industry practice.

This paper summarizes the report prepared by an IEEE PES Task Force. Resilience is a fairly new technical concept for power systems, and it is important to precisely delineate this concept for actual applications. As a critical infrastructure, power systems have to be prepared to survive rare but extreme incidents (natural catastrophes, extreme weather events, physical/cyber-attacks, equipment failure cascades, etc.) to guarantee power supply to the electricity-dependent economy and society. Thus, resilience needs to be integrated into planning and operational assessment to design and operate adequately resilient power systems. Quantification of resilience as a key performance indicator is important, together with costs and reliability. Quantification can analyze existing power systems and identify resilience improvements in future power systems. Given that a 100% resilient system is not economic (or even technically achievable), the degree of resilience should be transparent and comprehensible. Several gaps are identified to indicate further needs for research and development.

The design of cost-effective power line routes is a critical step towards elaborating a consistent network expansion plan. Although several attributes are considered by planning agents, the impact of sharp curvatures is typically neglected within the route design process, thus not accounting for the need for more expensive structures to support the power line. In this work, we tackle this issue by developing a holistic methodology that combines geoprocessing technologies with a novel combinatorial optimization technique to assist practitioners in identifying cost-effective smooth power line routes. For the latter, since the fundamental mathematical structure of the proposed tool falls into the class of a Quadratic Shortest Path Problem, an efficient dynamic programming algorithm is designed to handle the typical large-scale instances of power line tracing applications. The effectiveness of the proposed methodology is illustrated with a case study based on a real-world project of the Brazilian transmission sector.

Decarbonising and decontaminating remote regions in the world presents several challenges. Many of these regions feature isolation, dispersed demand in large areas, and a lack of economic resources that impede the development of robust and sustainable networks. Furthermore, isolated systems in the developing world are mostly based on diesel generation for electricity, and firewood and liquefied petroleum gas for heating, as these options do not require a significant infrastructure cost. In this context, we present a stochastic multi-energy multi-microgrid system planning model that integrates electricity, heat and hydrogen networks in isolated systems. The model is stochastic to capture uncertainty in renewable generation outputs, particularly hydro and wind, and thus design a multi-energy system proved secured against such uncertainty. The model also features two distinct constraints to limit the emissions of CO2 (for decarbonisation) and particulate matter (for decontamination), and incorporates firewood as a heating source. Moreover, given that the focus is on low-voltage networks, we introduce a fully linear AC power flow equations set, allowing the planning model to remain tractable. The model is applied to a real-world case study to design a multi-energy multi-microgrid system in an isolated region in Chilean Patagonia. In a case with a zero limit over direct CO2 emissions, the total system’s cost increases by 34% with respect to an unconstrained case. In a case with a zero limit over particulate matter emissions, the total system’s cost increases by 189%. Finally, although an absolute zero limit over both, particulate matter and direct CO2 emissions, leads to a total system’s cost increase of 650%, important benefits in terms of decarbonisation and decontamination can be achieved at marginal cost increments.

Price controls are commonly employed in Latin America and other regions to prevent price increases in exporting regions during cross-border trade of electricity. This paper introduces a methodology to evaluate the economic impact of price controls on generation investments, dispatch decisions, and carbon emissions in the context of cross-border electricity trade. We propose a non-linear adjusting function to represent a price-control rule, which can be incorporated into equilibrium models for generation firms and price-sensitive consumers. By employing fixed-point iterations between the adjusting function and an optimization problem that determines equilibrium investments and dispatch decisions for generation firms in a competitive environment, we obtain an equilibrium solution. We apply the proposed methodology to analyze the current price controls governing cross-border electricity trade between Chile and Argentina, with a focus on the exporting country. Our findings reveal that existing price controls can impede incentives for renewable energy investments, particularly in wind and solar, while stimulating investments in fossil fuel-based generation capacity. Moreover, these controls lead to increased CO2 emissions compared to a scenario without price controls. Additionally, we observe that the inefficiency resulting from price controls is proportionate to the volume of exports relative to local demand and the price elasticity of demand in the importing region.

Maintenance plans aim to reduce the frequency of failures by maintaining components before their state of degradation reaches an undesirable level. These plans must balance the cost of performing maintenance against their benefits, i.e., a reduction in supply interruptions. This paper proposes a stochastic cost-benefit framework to develop maintenance plans that optimally balances these costs and benefits on an array of randomly generated scenarios in a power network. We implement this framework through optimization via simulation. We simulate the effect of various maintenance actions on the availability and degradation state of network components, failures due to human errors, and system operation (including demand curtailments). Based on these results, we assess the costs and benefits of these actions and find the best stream of Maintenance and Inspection (M&I) actions scheduled in time. We apply this approach to the power system supplying a large industrial facility to assess the impact of both offline and online inspections, as well as preventive and corrective maintenance actions. We take advantage of the flexibility of the proposed approach to quantify the potential benefits of online condition monitoring, analyze strategies to hedge against simultaneous outages, and study the relative value of asset redundancy and maintenance plans in enhancing reliability.

In light of the rising frequency and impact of natural hazards on power systems, planning resilient network investments is becoming increasingly important. This task, however, needs, in addition to widely accepted investment options focused on installing new infrastructure, explicit recognition of investment propositions to harden existing infrastructure such as substations. Hardening networks is fundamentally challenging to incorporate in optimization problems since it affects outage probabilities. Therefore, we propose an optimization approach to determine optimal portfolios of resilient network investments, considering endogenous probabilities that change with hardening investment options. This decision-dependent-probability model finds the optimal network enhancements in a cost-benefit fashion, minimizing investment plus operational costs, including demand curtailments. The proposed model also considers distributed energy resources (DER), which can displace costly network investments. Additionally, the model takes into account the lack of fully accurate fragility curves; thus, outage probabilities are not only affected by hardening decisions but also by the inherent uncertainty associated with fragility modeling. This is a key concern in practical resilience assessment and is addressed in this work through a global-convergent exact algorithm. Case studies applied on earthquakes in Chile demonstrate the benefits of our proposed network planning approach.

The unprecedented pandemic raises a number of uncertainties and implications in achieving the goal of a clean electricity future. During the peak of COVID-19, more than 100 countries implemented full or partial lockdowns to contain the pandemic. The lockdowns had significant impacts on global renewable energy deployments. This article analyzes data collected from countries and regions around the world, from prepandemic to postpandemic times. We also analyze the challenges and opportunities for clean energy development brought by the pandemic. Visions and suggestions for postpandemic development are also presented. To provide the readers with a quick overview of the global clean energy development progress, we summarize the latest goals and achievements of different countries and regions in Table 1. More details are articulated in the following discussions.

Power system operators and planners are dealing both with the integration of unparalleled levels of variable renewable energy sources and deep uncertainties that originate from new technological developments, changing regulatory frameworks, unknown investment, operational costs of technologies, etc. An inadequate representation of the uncertainties may result in a substantial risk of deploying inflexible investment solutions incapable of adapting efficiently to evolving scenarios. In this context, this work studies the effects of increasing the granularity used to represent the long-term uncertainty by analysing its impact on the resulting optimal portfolios of new transmission lines, battery energy storage systems and pumped-hydro storage systems. The studies are conducted on an instance of the Australian power system described by the system operator for planning purposes, including four types of uncertainty granularity, namely deterministic representation, and 2-stage, 3-stage and 4-stage stochastic representations. To address the computational challenges associated with the large mixed-integer linear stochastic problems, the different instances are reformulated using Dantzig-Wolfe decomposition, enabling the use of a column generation approach to solve the investment problem. The case study applications show substantial adjustments in the investment portfolios as uncertainty granularity changes, with a clear tendency to increase battery storage investment as uncertainty is better represented.

Decarbonization processes around the world are pressing system operation significantly, particularly in terms of ensuring appropriate reliability and stability levels when integrating massive amounts of converter-based generation technologies (CGTs). In this context, this work proposes an optimization-simulation model to optimize system operation with nadir considerations via simulation of detailed system dynamic responses. We propose a Gauss–Seidel iterative method to incorporate the precise governors’ dynamic responses determined by the simulations within the sequential optimization problems. Through several case studies, we demonstrate the benefits of our approach and analyze the impacts of incorporating a detailed representation of units’ dynamic responses. We also compare our frequency nadir-constrained approach against an alternative inertia-constrained approach that ensures minimum amounts of inertia levels. Particularly, we show that ensuring minimum amounts of inertia may lead to inefficient economic outputs in terms of costs and integration of renewable generation without security gains in terms of frequency stability compared with constraining frequency nadir.

This paper analyzes the impacts of the first wave of COVID-19 (March 2020 - September 2020) on the electricity demand of different types of consumers in Chile, including residential, commercial, and industrial demand. We leverage data from 230 thousand smart meters of residential and commercial consumers in 32 communes of Santiago (the capital city of Chile), which allows us to investigate the evolution of their demands with an hourly temporal resolution. Additionally, we use demand data of large industrial consumers provided by the Chilean system operator to study the impact of the pandemic on different economic sectors. This paper demonstrates that the COVID-19 pandemic, and the associated containment measures, have featured a drastically different impact on the various types of consumers in Chile. In particular, we show that the demand of residential consumers has increased throughout the first wave, even when we isolate the effects of the pandemic from those related to weather. Furthermore, we study how these effects change in different communes of Santiago, contrasting our findings with the socio-economic levels of the population. In effect, we find different demand response patterns depending on the socio-economic background of consumers. We also show that commercial demand has significantly declined due to the containment measures implemented and that the hospitality and construction economic sectors have been the most affected in the country.

Resilience is a vital concept in engineering, business, and natural sciences, and is a measure of the ability of an entity to withstand High Impact Low Probability (HILP) events. During the COVID-19 pandemic, which started in late 2019/early 2020, power system utilities around the globe have responded in effective and efficient ways to enhance the resilience of their organisations, both in terms of real-time operations and prudent management of its infrastructure, in order to continue their mandate in providing reliable supply to meet customer demands. This paper presents the CIGRE definition for power system resilience, established by the C4.47 Working Group in 2018, and demonstrates the application of resilience-oriented thinking within the electrical sector. The response and recovery efforts are described, with respect to the key actionable measures integral to the power system resilience definition, taken before, during and after the COVID-19 pandemic. A practical conceptual framework is also presented for thinking about resilience in terms of three key components of resilience strategies: organisational, infrastructure and operational resilience. The paper also discusses the different strategies adopted in response to COVID-19, based on the C4.47 members’ experiences during the pandemic. Finally, a case study is presented, which proves the effectiveness of a set of response measures, using graph theory and the characteristics of the staff-asset interactions.

In recent years, countries around the world have been severely affected by catastrophic wildfires with significant environmental, economic, and human losses. Critical infrastructures, including power systems, have been severely damaged, compromising the quality of life and the continuous and reliable provision of essential services, including the electricity supply.

Several extreme wildfires have recently affected many countries worldwide, causing enormous economic and human losses and vastly damaging ecosystems. For example, in January 2017, a highly destructive series of wildfires destroyed more than 500,000 hectares in Chile, burned 3,000 houses, and left 11 dead. The intensity and impact of these wildfires had never been experienced before in the country.

Variable Renewable Energy (VRE), i.e., wind and solar photovoltaics (PVs), is being installed in rapidly increasing amounts around the world. Growth in VRE is being spurred by ambitious zero-carbon targets set by countries and individual states across the globe. The European Union approved a carbon neutrality target for 2050 in 2019. Japan’s newly appointed prime minister announced the same target in 2020, and the Chinese government set goals to peak carbon emissions before 2030 and become carbon neutral by 2060.

This paper studies how environmental policies, such as renewable portfolio standards (RPS) and carbon taxes, might contribute to reducing risk exposure in the electricity generation sector. We illustrate this effect by first computing long-term market equilibria of the Chilean generation sector for the year 2035 using a risk-averse planning model, considering uncertainty of hydrological scenarios and fossil fuel prices as well as distinct levels of risk aversion, but assuming no environmental policies in place. We then compare these risk-averse equilibria to generation portfolios obtained by imposing several levels of RPS and carbon taxes in a market with risk-neutral firms, separately. Our results show that the implementation of both policies can provide incentives for investments in portfolios of generation technologies that limit the risk exposure of the system, particularly when high levels of RPS (35%) or high carbon taxes (35 $/tonCO2) are applied. However, we find that in the case of a hydrothermal system, the resulting market equilibria under RPS policies yield expected generation cost and risk levels (i.e. standard deviation of costs) that are more similar to the efficient portfolios determined using a risk-averse planning model than the ones we find under the carbon tax.

Natural hazards cause major power outages as a result of spatially-correlated failures of network components. However, these correlations between failures of individual elements are often ignored in probabilistic planning models for optimal network design. We use different types of planning models to demonstrate the impact of ignoring correlations between component failures and the value of flexible transmission assets when power systems are exposed to natural hazards. We consider a network that is hypothetically located in northern Chile, a region that is prone to earthquakes. Using a simulation model, we compute the probabilities of spatially-correlated outages of transmission and substations based on information about historical earthquakes in the area. We determine optimal network designs using a deterministic reliability criterion and probabilistic models that either consider or disregard correlations among component failures. Our results show that the probability of a simultaneous failure of two transmission elements exposed to an earthquake can be up to 15 times higher than the probability simultaneous failure of the same two elements when we only consider independent component failures. Disregarding correlations of component failures changes the optimal network design significantly and increases the expected levels of curtailed demand in scenarios with spatially-correlated failures. We also find that, in some cases, it becomes optimal to invest in HVDC instead of AC transmission lines because the former gives the system operator the flexibility to control power flows in meshed transmission networks. This feature is particularly valuable to systems exposed to natural hazards, where network topologies in post-contingency operating conditions might differ significantly from pre-contingency ones.

Increasing power system stability challenges are being witnessed worldwide, while transitioning toward low-carbon grids with a high-share of power electronic converter (PEC)-interfaced renewable energy sources (RESs) and distributed energy resources (DERs). Concurrently, new technologies and operational strategies are being implemented or proposed to tackle these challenges. Since electricity grids are deregulated in many jurisdictions, such technologies need to be integrated within a market framework, which is often a challenge in itself due to inevitable regulatory delays in updating grid codes and market rules. It is also highly desirable to ensure that an economically feasible optimal technology mix is integrated in the power system, without imposing additional burdens on electricity consumers. This article provides a comprehensive overview of emerging power system stability challenges posed by PEC-interfaced RES and DER, particularly related to low inertia and low system strength conditions, while also introducing new technologies that can help tackle these challenges and discussing the need for suitable techno-economic considerations to integrate them into system and market operation. As a key point, the importance of recognizing the complexity of system services to guarantee stability in low-carbon grids is emphasized, along with the need to carefully integrate new grid codes and market mechanisms in order to exploit the full benefits of emerging technologies in the transition toward ultra-low carbon futures.

In recent years, the transition towards low-carbon electricity systems has increased the development of renewable generation and, in turn, of transmission infrastructure. Importantly, developing low-carbon technologies (that are generally located far from load centers) and their associated network infrastructure, may conflict with land uses that are valuable by society (e.g. the presence of national parks, indigenous development, touristic zones, etc.). Appropriately addressing this conflict is key for policy makers and regulators to foster an effective, sustainable, and socially acceptable system expansion. In this context, this work analyzes the effects of accounting for these land-use, socio-environmental externalities on the expansion of the entire power system. For a more effective mitigation of system expansion impacts on land uses, we propose to coordinate the needed investments among the various market participants such as generation developers and network planners. To assess this proposal, we develop a two-stage stochastic program that determines the future generation and network expansions considering both (i) a balance between monetary/investment costs and their corresponding socio-environmental externality costs (derived from the land-use impacts of new electricity investments), and (ii) different levels of coordination among market participants. Hence, we can assess the benefits of various coordination strategies against the actual approach to system expansions with no coordination among developers. By running various case studies based on the Chilean electricity system by 2030, we show that recognition of socio-environmental externalities at the moment of deciding system expansions can have a significant impact on the location of future infrastructure and, remarkably, on the entire mix of new generation projects. Particularly, we found an increase in bulk, transmission-connected solar power generation capacity by circa 25% when land-use externalities are considered in the system expansion problem. This is so because bulk solar power generation projects tend to present less socio-environmental impacts (since the solar power potential is generally higher in deserts and arid regions, away from populated areas) and, up to a certain extent, have the ability to displace the need for other generation technologies, particularly hydropower, located in areas with significantly conflicting land uses. We also demonstrated the benefits of investment coordination in supporting both an increased penetration of solar power generation, and an economically effective and sustainable development of a low-carbon power system in Chile.

Chile was the first country that privatized all generation, transmission, and distribution services, and introduced competition in the generation segment. Nearly four decades after its creation, many features of the original electricity market design remain unchanged. In this paper, we provide a brief history of the Chilean electricity market and explain its main limitations going forward. Some of these include the use of a cost-based mechanism for spot transactions based on a merit-order curve, low temporal granularity of spot prices, missing forward markets to settle deviations from day-ahead commitments, inefficient pricing of greenhouse gas emissions due to administrative rules, and a capacity mechanism that does not reflect a clear resource adequacy target. Many of these limitations are also present in other electricity markets in Latin America that, when privatized, mirrored many features of the electricity market design in Chile. Failing to address these limitations will provide distorted incentives for the efficient entry and operation of resources that could impart flexibility to the system, increasing the cost of decarbonizing the power sector.

We demonstrate that environmental policies can decrease the risks in energy generation for private investors when several renewable technologies are simultaneously triggered. This is because diverse renewable technologies can hedge the intermittent generation of other forms of renewable power. Our study is distinct from previous literature, which has not examined environmental policies through a risk-reduction analysis, or has only considered a few technologies—such as wind and solar—when analyzing risk-reduction benefits. This paper is important, as environmental policies can be justified not only due to environmental benefits, but also economically from a risk-reduction perspective by using basic diversification gains.

Microgrids have the potential to provide security and flexibility to power systems through the integration of a wide range of resources, including distributed energy storage, usually in the form of batteries. An aggregation of microgrids can enable the participation of these resources in the main system’s energy and ancillary services market. The traditional minimum-cost operation, however, can undermine microgrid’s ability to hold reserve capacity for operation in islanded mode and can rapidly degrade distributed batteries. This paper studies the impacts of various operational strategies from distributed energy storage plants on their revenues and on market prices, considering an array of microgrids that act in a synchronized fashion. The operational model minimizes the entire electric power system cost, considering transmission-connected and distributed energy resources, and capturing capacity degradation of batteries as part of the cost function. Additionally, microgrid-based, distributed batteries can provide energy arbitrage and both system-level and microgrid-level security services. Through several case studies, we demonstrate the economic impacts of distributed energy storage providing these services, including also capacity degradation. We also demonstrate the benefits of providing reserve services in terms of extra revenue and battery lifespan. Finally, we conclude that limitations in the provision of system-level services from distributed batteries due to degradation considerations and higher microgrid-level security requirements may, counterintuitively, increase system-level revenues for storage owners, if such degradation considerations and microgrid-level security requirements are adopted, at once, by a large number of microgrids, leading to unintended, non-strategic capacity withholding by distributed storage owners.

Reservoir operations often require balancing among several water uses. Despite the non-consumptive nature of hydropower, conflicts exist between irrigation and hydropower due to a demand seasonality mismatch. Hydropower operations are scheduled as part of a large-scale power grid, whereas irrigation decisions takes place at a smaller scale, most often the river basin. Balancing these water uses should involve a co-optimization at the power grid level, integrating all basins contributing hydropower to the grid. However, grid-wide co-optimization is not always possible due, for instance, to separate regulatory settings between water uses. For those cases, we propose a basin-wide co-optimization approach that integrates two decision scales—power grid and river basin— into a hydro-economic model. Water for irrigation is usually allocated by water rights or binding contracts, represented as constraints on grid-wide power operation models. We propose a water allocation scheme that integrates monthly marginal benefits of water for irrigation and hydropower at the basin level. Monthly water demand functions for irrigation are developed using an agricultural economic model, and marginal benefits of hydropower production are derived from a cost-minimization, grid-wide power scheduling model. Results for 50 inflow scenarios show that the proposed basin-wide co-optimization provides an economically sound operation. Total benefits from water use in the basin are on average 2.5% higher than those obtained under mandatory irrigation. Moreover, expected benefits under co-optimization are 5.4% and 1.8% higher for irrigated agriculture and hydropower, respectively, alleviating the conflicts between water uses in the basin.

Although extreme events, mainly natural disasters and climate change-driven severe weather, are the result of naturally occurring processes, power system planners, regulators, and policy makers do not usually recognize them within network reliability standards. Instead, planners have historically designed the electric power infrastructure accounting for the so-called credible (or "average") outages that usually represent single or (some kind of) simultaneous faults (e.g., faults on double circuits).

Following the trend observed in developed economies, various Latin American governments are committed to reducing greenhouse gas emissions, particularly in the power sector. In countries such as Chile, Peru, Colombia, Brazil, and Mexico, various regulatory policies have been issued to meet renewable-generation integration targets and satisfy the increasing demand from consumers for supply quality. Meanwhile, the integration of distributed generation (DG) in rural and urban areas as well as the increasing need to integrate electric vehicles (EVs) in urban areas are driving important reforms in the distribution sector.

The supply of natural gas is generally based on contracts that are signed prior to the use of this fuel for power generation. Scarcity of natural gas in systems where a share of electricity demand is supplied with gas turbines does not necessarily imply demand rationing, because most gas turbines can still operate with diesel when natural gas is not available. However, scarcity conditions can lead to electricity price spikes, with welfare effects for consumers and generation firms. We develop a closed-loop equilibrium model to evaluate if generation firms have incentives to contract or import the socially-optimal volumes of natural gas to generate electricity. We consider a perfectly-competitive electricity market, where all firms act as price-takers in the short term, but assume that only a small number of firms own gas turbines and procure natural gas from, for instance, foreign suppliers in liquefied form. We illustrate an application of our model using a network reduction of the electric power system in Chile, considering two strategic firms that make annual decisions about natural gas imports in discrete quantities. We also assume that strategic firms compete in the electricity market with a set of competitive firms do not make strategic decisions about natural gas imports (i.e., a competitive fringe). Our results indicate that strategic firms could have incentives to sign natural gas contracts for volumes that are much lower than the socially-optimal ones, which leads to supernormal profits for these firms in the electricity market. Yet, this effect is rather sensitive to the price of natural gas. A high price of natural gas eliminates the incentives of generation firms to exercise market power through natural gas contracts.

Although extreme natural disasters have occurred all over the world throughout history, power systems planners do not usually recognize them within network investment methodologies. Moreover, planners had historically focused on reliability approaches based on average (rather than risk) performance indicators, undermining the effects of high impact and low probability events on investment decisions. To move towards a resilience centred approach, we propose a practical framework that can be used to identify network investments that offer the highest level of hedge against risks caused by natural hazards. In a first level, our framework proposes network enhancements and, in a second level, uses a simulation to evaluate the resilience level improvements associated with the network investment propositions. The simulator includes 4 phases: threat characterization, vulnerability of systems components, system response, and system restoration, which are simulated in a sequential Monte Carlo fashion. We use this modeling framework to find optimal portfolio solutions for resilient network enhancements. Through several case studies with applications to earthquakes, we distinguish the fundamental differences between reliability- and resilience-driven enhancements, and demonstrate the advantages of combining transmission investments with installation of backup distributed generation.

Chile was the first country in Latin America to impose a tax on carbon-emitting electricity generators. However, the current regulation does not allow firms to include emission charges as costs for the dispatch and pricing of electricity in real time. The regulation also includes side-payment rules to reduce the economic losses of some carbon-emitting generating units. In this paper we develop an equilibrium model with endogenous investments in generation capacity to quantify the long-run economic inefficiencies of an emissions policy with such features in a competitive setting. We benchmark this policy against a standard tax on carbon emissions and a cap-and-trade program. Our results indicate that a carbon tax with such features can, at best, yield some reductions in carbon emissions at a much higher cost than standard emission policies. These findings highlight the critical importance of promoting short-run efficiency by pricing carbon emissions in the spot market in order to incentivize efficient investments in generating capacity in the long run.

Distributed energy resources (DER) have the potential to significantly contribute to network security and hence release latent capacity of existing transmission assets. In this context, we propose a distributionally robust approach to network security in order to recognize the limited data and knowledge associated with the underlying process behind the realization of system contingencies within the transmission expansion planning (TEP) problem, and thus determine the optimal portfolio ofDERservices necessary to displace, in a secure fashion, inefficient network investments. To do so, we propose a two-stage optimization model where the first stage determines the transmission expansion plan and the scheduling of DER post-contingency services in coordination with further corrective control measures such as generation reserves. The second stage minimizes the expected cost of corrective actions under various contingencies. Through various case studies, we demonstrate the benefits of security services provided by DER and the advantages of our proposed distributionally robust approach (where outage rates are assumed ambiguous) against alternative n − K security and stochastic approaches, where outage rates are either ignored or assumed fully known, respectively.

This paper analyzes the impact of modeling detail in long-term energy planning models when assessing the value of energy storage in electricity markets. By running six optimization models for the long-term planning of combined generation and storage installed capacities in the Chilean electricity system (each with different levels of detail/complexity in terms of time resolution, recognition of operational inflexibility —i.e. technical constraints of power plants— and recognition of uncertainty in fossil fuel prices), we determine six portfolio solutions with significantly different levels of energy storage installed capacity. Furthermore, we found that the total installed capacity of storage plants escalates when increasing the level of modeling complexity, which can be achieved by augmenting the time resolution and the number of constraints that better recognize the inflexibility of generation plants and by acknowledging the presence of long-term uncertainties associated with fossil fuel prices fluctuations. In our particular study, we found a difference of more than an order of magnitude between the amount of installed capacity of storage plants determined by the detailed model (that with hourly resolution and full consideration of technical constraints of power plants) and that obtained by the planning model that adopts the traditional assumptions commonly utilized in regulatory offices around the word (i.e. low time resolution and no recognition of technical/unit commitment constraints and uncertainty). Particularly, we found that the traditional, simplified solution can deliver an installed capacity of storage plants as low as 240 MW (∼1.3% of estimated peak demand), while one of the most sophisticated solutions (which recognizes technical constraints of generating units, but ignores risks) delivers 7.8 GW (∼41.7% of estimated peak demand). Moreover, by running a risk-constrained stochastic planning model, we also determine a risk-averse portfolio solution, which demonstrated the increased value of energy storage capacity in reducing electricity cost risk.

The need to decarbonize the power sector through increased participation of renewable generation has originated an escalating necessity for transmission network investments that can be undertaken by a number of market participants, including planning authorities/system operators, network companies and project developers. The expansion of the power network, however, presents various environmental and social conflicts, in particular, with land uses that are valuable by society such as the presence of communities, national parks, protected forests, tourism zones, archaeological sites, etc. In this context of environmental and social awareness, we assess the benefits of two strategies that coordinate network investments among various participants and compare them against the current counterfactual approach, where no coordination is undertaken and thus renewable generation projects are connected to the main transmission system in an individual, project-by-project basis. Through various case studies based on the main Chilean transmission system, we show that the lack of coordination in network investments may present severe impacts in terms of the socio-environmental externalities of transmission network expansions. Furthermore, we demonstrate that attempting to reduce externalities of new network investments without proper coordination of new developments may significantly limit the success of a land use policy associated with network developments.

Planning networks within a multi-stage stochastic framework is becoming critical for improving the economic performance of investment decisions against the present levels of uncertainty. This problem, however, has been proved extremely challenging to be solved on real networks, especially when considering the interactions among various energy vectors. In this context, this paper proposes the use of Dantzig-Wolfe decomposition and parallel asynchronous column generation to solve a multi-stage stochastic planning of an integrated power and natural gas system, including non-linear effects of gas compressors reformulated in a mixed integer linear programming fashion. We compare the computational performance of the proposed approach against two alternatives: a parallel synchronous column generation approach and the counterfactual, monolithic approach, where the mixed integer linear program (without decomposition) is directly solved by a commercial solver. Our sources of long-term uncertainty are the locations and volumes of (i) new renewable generation (which may depend on policy objectives, regulatory incentives, etc. that are constantly evolving) and (ii) new demands. The model also ensures that the planned energy infrastructure can effectively be operated reliably against a large array of operating conditions originated by high variability of renewable generation outputs, multiple demand levels and hydro inflows. Through various case studies, we discuss and demonstrate the importance of stochastic and integrated planning of electricity and natural gas systems along with the benefits of asynchronous algorithms and decomposition techniques that can be parallelized.

This paper studies key aspects of preventive and corrective security strategies to coordinate flexible transmission network infrastructure. To do so, we propose a 2-stage stochastic optimization model that can efficiently coordinate available preventive and corrective control actions (pre- and post-contingency) from flexible network technologies, generation and demand, while explicitly considering the likelihood of post-contingency events and wind/solar uncertainty. This stochastic/probabilistic model constitutes the counterfactual against which current deterministic, preventive operational practices are compared. Flexible network equipment such as HVDC and FACTS devices are efficiently modelled through a tight MILP representation and network losses are also included through a linear representation. Through several case studies, we demonstrate the advantages of probabilistic, corrective security to improve coordination of HVDC and FACTS setpoints and thus reduce network congestion and reserve holding levels, improving the overall efficiency of the system operation. We also quantify the value loss associated with current deterministic, preventive control operational practices when coordinating setpoints of flexible network equipment, demonstrating that adding flexible network technology could even increase system costs under a preventive security approach.

Microgrids are emerging to coordinate distributed energy resources and locally increase reliability to expected events and resilience to extreme events. Furthermore, by deploying their inherent flexibility, grid-connected microgrids are capable to provide different services at the system-level too. However, these are often assessed independently and a comprehensive integrated framework that can assess benefits for the whole power system is missing. In this outlook, this paper introduces a system-level assessment framework based on the concept of different duration’s reserve services that can be provided by microgrids to the main electricity grid in response to both credible (reliability-oriented) and extreme, possibly unforeseen (resilience-oriented) contingencies. Probabilistic capacity tables accounting for different sources of uncertainty related to both microgrids’ operation and occurrences of unfavorable events are built to assess the microgrids’ potential capacity contribution to a particular reserve service. Case studies based on representative microgrids and a British test system clearly illustrate and quantify how aggregation of microgrids could provide significant contribution to both short-term reliability and longer-duration resilience services far beyond the simple summation of the individual contributions, thus demonstrating a clear synergic effect, as well as the key role played by different forms of energy storage. The proposed framework can assist policy makers and regulators on the strategic role of microgrids for energy system planning and policy developments, including design of ancillary services markets not only to enhance system reliability but also resilience.

The use of energy storage devices, such as lithium-ion batteries, has become popular in many different domains and applications. Hence, it is relatively easy to find literature associated with problems of battery state-of-charge estimation and energy autonomy prognostics. Despite this fact, the characterization of battery degradation processes is still a matter of ongoing research. Indeed, most battery degradation models solely consider operation under nominal (or strictly controlled) conditions, although actual operating profiles (including discharge current) may differ significantly from those. In this context, this article proposes a lithium-ion battery degradation model that incorporates the impact of arbitrary discharge currents. Also, the proposed model, initially calibrated through data reported for a specific lithium-ion battery type, can characterize degradation curves for other lithium-ion batteries. Two case studies have been carried out to validate the proposed model, initially calibrated by using data from a Sony battery. The first case study uses our own experimental data obtained for a Panasonic lithium-ion cell, which was cycled and degraded at high current rates. The second case study considers the analysis of two public data sets available at the Prognostics Center of Excellence of NASA Ames Research Center website, for batteries cycled using nominal and 2-C (twice the nominal) discharge currents. Results show that the proposed model can characterize degradation processes properly, even when cycles are subject to different discharge currents and for batteries not manufactured by Sony (whose data were used for the initial calibration).

Centralized energy auctions for long-term contracts are commonly-used mechanisms to ensure supply adequacy, to promote competition, and to protect retail customers from price spikes in Latin America. In Chile, the law mandates that all distribution companies must hold long-term contracts—which are awarded on a competitive centralized auction—to cover 100% of the projected demand from three to fifteen years into the future. These contracts can be indexed to a series of financial parameters, including fossil fuel prices at reference locations. Drawing from portfolio theory, we use a simple example to illustrate the difficulties of selecting, through the current clearing mechanism that focuses on average costs and individual characteristics of the offers, a portfolio of long-term energy contracts that could simultaneously minimize the expected future cost of energy and limit the risk exposure of retail customers. In particular, we show that if the objective of the regulator is to limit the risk to regulated consumers, it could be optimal to include contracts that would not be selected based on individual characteristics of the offers and a least-cost auction objective, but that could significantly reduce the price variance of the overall portfolio due to diversification effects between indexing parameters.

The benefits of new transmission investment significantly depend on deployment patterns of renewable electricity generation that are characterized by severe uncertainty. In this context, this paper presents a novel methodology to solve the transmission expansion planning (TEP) problem under generation expansion uncertainty in a min-max regret fashion, when considering flexible network options and n-1 security criterion. To do so, we propose a five-level mixed integer linear programming (MILP) based model that comprises: (i) the optimal network investment plan (including phase shifters), (ii) the realization of generation expansion, (iii) the co-optimization of energy and reserves given transmission and generation expansions, (iv) the realization of system outages, and (v) the decision on optimal post-contingency corrective control. In order to solve the five-level model, we present a cutting plane algorithm that ultimately identifies the optimal min-max regret flexible transmission plan in a finite number of steps. The numerical studies carried out demonstrate: (a) the significant benefits associated with flexible network investment options to hedge transmission expansion plans against generation expansion uncertainty and system outages, (b) strategic planning-under-uncertainty uncovers the full benefit of flexible options which may remain undetected under deterministic, perfect information, methods and (c) the computational scalability of the proposed approach.

Any Cost-effective transition toward low-carbon electricity supply will necessitate improved system flexibility to address the challenges of increased balancing requirements and degradation in asset use. Energy storage (ES) represents a flexible option that can bring significant, fundamental economic benefits to various areas in the electric power sector, including reduced investment requirements for generation, transmission, and distribution infrastructure as well as reduced system operation and balancing costs. The additional flexibility offered by ES could also significantly reduce the requirement for investment in low-carbon generation capacity while achieving the established carbon intensity targets. Moreover, ES may present significant option value, as it can provide flexibility for dealing with uncertainty in future system development.

It is well known that storage facilities can provide value to various electricity sectors through several services, which we group into five main classes.

Electricity grid operators and planners need to deal with both the rapidly increasing integration of renewables and an unprecedented level of uncertainty that originates from unknown generation outputs, changing commercial and regulatory frameworks aimed to foster low-carbon technologies, the evolving availability of market information on feasibility and costs of various technologies, etc. In this context, there is a significant risk of locking-in to inefficient investment planning solutions determined by current deterministic engineering practices that neither capture uncertainty nor represent the actual operation of the planned infrastructure under high penetration of renewables. We therefore present an alternative optimization framework to plan electricity grids that deals with uncertain scenarios and represents increased operational details. The presented framework is able to model the effects of an array of flexible, smart grid technologies that can efficiently displace the need for conventional solutions. We then argue, and demonstrate via the proposed framework and an illustrative example, that proper modelling of uncertainty and operational constraints in planning is key to valuing operationally flexible solutions leading to optimal investment in a smart grid context. Finally, we review the most used practices in power system planning under uncertainty, highlight the challenges of incorporating operational aspects and advocate the need for new and computationally effective optimization tools to properly value the benefits of flexible, smart grid solutions in planning. Such tools are essential to accelerate the development of a low-carbon energy system and investment in the most appropriate portfolio of renewable energy sources and complementary enabling smart technologies. This article is part of the themed issue 'Energy management: flexibility, risk and optimization'.

The notion of developing a transnational offshore grid in the North Sea has attracted considerable attention in the past years due to its potential for substantial capital savings and increased scope for cross-border trade, sparking a European-wide policy debate on incentivizing integrated transmission solutions. However, one important aspect that has so far received limited attention is that benefits will largely depend on the eventual deployment pattern of electricity infrastructure which is currently characterized by severe locational, sizing and timing uncertainty. Given the lack of coordination between generation and network developments across Europe, there is a real risk for over-investment or a premature lock-in to options that exhibit limited adaptability. In the near future, important choices that have to be made concerning the network topology and amount of investment. In this paper we identify the optimal, in terms of reduced cost, network investment (including topology) in the North Seas countries under four deployment scenarios and five distinct policy choices differing in the level of offshore coordination and international market integration. By drawing comparisons between the study results, we quantify the net benefit of enabling different types of coordination under each scenario. Furthermore, we showcase a novel min–max regret optimization model and identify minimum regret first-stage commitments which could be deployed in the near future in order to enhance strategic optionality, increase adaptability to different future conditions and hence reduce any potential sub-optimality of the initial network design. In view of the above, we put forward specific policy recommendations regarding the adoption of a flexible anticipatory expansion framework for the identification of attractive investment opportunities under uncertainty.

In this paper, the efficacy and value of using corrective control supported by transient assistive measures (TAM) is quantified in terms of the cost savings due to less constrained operation of the system. The example TAM is a rapid modulation of the power order of the High Voltage Direct Current (HVDC) links in the system so as to improve transient stability during corrective control. A sequential approach is used for the offline value assessment: a security constrained economic dispatch (SCED) module (master problem) determines the optimal generation dispatch, HVDC settings and the corrective control actions to be used post-fault (generation and demand curtailed) so as to minimize the operational costs while ensuring static security. The transient stability module (slave problem) assesses the dynamic stability for the operating condition set by the SCED and, if needed, applies appropriate TAM to maintain the system transiently stable. If this is not possible, the master module uses a tighter set of security constraints to update the dispatch and other settings until the system can be stabilized. A case-study on the Great Britain system is used to demonstrate that corrective control actions supported by TAM facilitate significantly higher pre-fault power transfers whilst maintaining N-2 security.

In an electricity market environment, energy storage plant owners are remunerated for the provision of services to multiple electricity sectors. Some of these services, however, may accelerate battery aging and degradation and hence this needs to be properly balanced against associated services remunerations. In this framework, we propose a combined economic-degradation model to quantify effects of operational policies (mainly focused on constraining State of Charge –SOC– to prescribed levels in order to reduce effects of aging) on gross revenue, multi-service portfolios, degradation and lifespan of distributed energy storage plants that can provide multiple services to energy and balancing market participants and Distribution Network Operators (DNO). Through various case studies based on the Great Britain (GB) system, we demonstrate that although operational policies focused on battery damage reduction will lead to a revenue loss in the short-term, such loss can be more than compensated by long-term revenues due to a lengthier battery lifespan. We also demonstrate that operational policies to reduce battery degradation mainly affect services related to the energy (rather than balancing) market, which represents a smaller proportion of the overall revenue streams of a distributed storage plant. The model is also used to study effects of ambient temperature fluctuations.

Integration of renewable generation can lead to both diversification of energy sources (which can improve the overall economic performance of the power sector) and cost increase due to the need for further resources to provide flexibility and thus secure operation from unpredictable, variable and asynchronous generation. In this context, we propose a cost-risk model that can properly plan generation and determine efficient technology portfolios through balancing the benefits of energy source diversification and cost of security of supply through the provision of various generation frequency control and demand side services, including preservation of system inertia levels. We do so through a scenario-based cost minimization framework where the conditional value at risk (CVaR), associated with costs under extreme scenarios of fossil fuel prices combined with hydrological inflows, is constrained. The model can tackle problems with large data sets (e.g. 8760 hours and 1000 scenarios) since we use linear programming and propose a Benders-based method adapted to deal with CVaR constraints in the master problem. Through several analyses, including the Chilean main electricity system, we demonstrate the effects of renewables on hedging both fossil fuel and hydrological risks; effects of security of supply on costs, risks and renewable investment; and the importance of demand side services in limiting risk exposure of generation portfolios through encouraging risk mitigating renewable generation investment.

The high penetration of renewables envisaged for future power systems will significantly increase the need for flexible operational measures and generation technologies, whose associated investment decisions must be properly planned in the long term. To achieve this, expansion models will need to incorporate unit commitment constraints, which can result in large scale MILP problems that require significant computational resources to be solved. In this context, this paper proposes a novel Dantzig–Wolfe decomposition and a column generation approach to reduce the computational burden and overcome intractability. We demonstrate through multiple case studies that the proposed approach outperforms direct application of commercial solvers, significantly reducing both computational times and memory usage. Using the Chilean power system as a reference case, we also confirm and highlight the importance of considering unit commitment constraints in generation expansion models.

Energy storage can provide services to several sectors in electricity industry, including generation, transmission and distribution, where conflicts and synergies may arise when storage is used to manage network congestion and provide services in energy and balancing markets. In this context, this study proposes an optimisation model to coordinate multiple services delivered to various market participants that uses corrective actions to resolve conflicts between provision of distribution network services (e.g. congestion and security of supply) and other services. The model maximises storage profit by scheduling active and reactive power to provide portfolio of services including distribution network congestion management, energy price arbitrage, frequency response and reserve services remunerated at different prices. The authors demonstrate that adopting corrective security to provide network services and deal with network congestion in a post-fault fashion, is overall more beneficial despite the energy needed to be stored during pre-fault conditions for applying post-contingency actions right after a network fault occurs. Furthermore, the authors’ analysis shows that application of corrective security can benefit both (i) storage owners through increased revenues in energy and balancing services markets and (ii) Distribution Network Operators through reduction in payments to storage owners and increased utilisation of network infrastructure.

Energy storage has the potential to provide multiple services to several sectors in electricity industry and thus support activities related to generation, network and system operation. Hence aggregating the value delivered by storage to these sectors is paramount for promoting its efficient deployment in the near future, which will provide the level of flexibility needed to deal with the envisaged high renewables share and the increase in peak demand driven by transport and heating electrification. In this context, we develop a Mixed Integer Linear Programming (MILP) model to schedule operation of distributed storage by coordinating provision of a range of system services which are rewarded at different market prices. The model maximises distributed storage’s net profit while providing distribution network congestion management, energy price arbitrage and various reserve and frequency regulation services through both active and reactive power control. We demonstrate benefits associated with the coordination of these services and its impacts on commercial strategies to determine optimal multi-service portfolios in the long term. We also demonstrate the value of reactive power control to support not only distribution network congestion management, but also efficient trading of energy and balancing services which are usually treated through active power-only control. In addition, we use the model to price the service of distribution network congestion management and propose an efficient investment policy to upgrade distribution network capacity in the presence of distributed storage. Finally, several case studies under current market conditions in Great Britain (GB) demonstrate that distributed storage revenues associated with frequency control services are significantly more profitable.

In Great Britain (GB) and across Europe significant investment in electricity transmission is expected over the coming years as decarbonisation and market integration efforts are intensified. However, there is also significant uncertainty with the amount, location and timing of new generation connection, which in turn will drive the transmission investment needs. Given the absence of efficient market design, we identify three key areas of concern with the current transmission investment arrangements: (i) a mis-aligned incentives framework for transmission investment and operation; (ii) lack of coordination of investment and operation; and (iii) conflicts of interest. We then propose three options for future evolution of transmission regimes, which cover the full spectrum of institutional arrangements with respect to transmission planning and delivery, i.e. how and who plans, owns, builds and operates the transmission system. For each option we present: key characteristics; evolution of the current regimes; the ability of the option to address the concerns; and key strengths and weaknesses. Overall, we conclude in the case of GB (this conclusion could be extended to other European countries) that the most appropriate option would be that of an Independent System Operator (ISO) who would be responsible for planning and operating the transmission system.

This paper demonstrates that the growth in application of corrective actions to enhance network utilization will require a probabilistic treatment of network security for determining efficient levels of investment in network reinforcement. A Benders decomposition based two-stage probabilistic optimization model for the operational and investment problems is proposed. For selecting relevant contingencies (beyond N-1 criteria), a novel filtering technique for efficient elimination of redundant outages is presented and successfully tested. In 2 numerical examples we compare efficiency of network reinforcement propositions under both deterministic and probabilistic frameworks, while optimizing available preventive and corrective control actions, and in particular focusing on the application of generation reserve in combination with special protection schemes (SPS) for network congestion management purposes. We highlight the inadequacies of the deterministic approach with respect to its inherent inability to optimize accurately the portfolio of pre-fault post-fault actions since the impacts of corrective actions (in the form of SPS, demand response) and occurrence of “non-credible” events require explicit consideration of the likelihood of various outages. We conclude that deterministic approach drives less efficient and potentially more risky system operation that ultimately leads to inefficient network investment.

The growing interest in decarbonizing electricity systems, together with advances in communication and information technologies that may support the application of demand and generation solutions to solve network problems, has initiated reviews of traditional operational practices and security grid standards in a number of jurisdictions. The key concern is that these historical practices and standards, mostly developed in the 1950s, may be inappropriate for the new emerging system as they may pose entry barriers for both renewable generation and Smart Grid technologies. In this context, the current paper presents a probabilistic cost–benefit framework for the development of future efficient operating strategies and network security standards enabled by new technology. By optimally balancing the costs of network constraints with various operational measures composed of preventive and corrective control actions, considering potential outages of network and generation facilities, optimal network capacity that should be released to network users in real time is determined. This framework is compatible with Smart Grid concepts integrating new generation, network, and demand technology. The study demonstrates that any attempt to fix a single generic rule for operating the network, as in the present deterministic standards, will lead to potentially significant inefficiencies. It is also shown that various operational measures (such as generation and demand response) can be effectively used to release additional network capacity. The results suggest that the probabilistic approach provides the basis for the development of future network operation and design standards that would maximize the value of the transmission network to users, enhance the utilization of existing networks, foster the entrance of new technologies that may complement and provide alternative network reinforcement, and hence facilitate efficient integration of renewable generation.

Electricity Systems Worldwide FACE Challenges of unprecedented proportions. In response to the climate change crisis, the governments of a number of countries are already committed to the support of renewable and other low-carbon generation technologies for the next ten years. Over the medium and long term, it is expected that the electricity sector in some jurisdictions will be almost entirely decarbonized and that there will be significantly increased levels of electricity production and demand, driven by the incorporation of the heating and transport sectors into the electric grid.

The implementation of auctions of long-term electricity contracts is arising as an alternative to ensure generation investment and therefore achieve a reliable electricity supply. The aim is to reconcile generation adequacy with efficient energy purchase, correct risk allocation among investors and consumers, and the politico-economic environment of the country. In this paper, a generic proposal for a long-term electricity contracts approach is made, including practical design concepts for implementation. This proposal is empirically derived from the auctions implemented in Brazil and Chile during the last 6 years. The study is focused on practices and lessons which are especially useful for regulators and policy makers that want to facilitate the financing of new desirable power plants in risky environments and also efficiently allocate supply contracts among investors at competitive prices. Although this mechanism is generally seen as a significant improvement in market regulation, there are questions and concerns on auction performance that require careful design and which are identified in this paper. In addition, the experiences and proposal described can serve to derive further mechanisms in order to promote the entrance of particular generation technologies, e.g. renewables, in the developed world and therefore achieve a clean electricity supply.