Game-theoretic modeling of curtailment rules and network investments with distributed generation

Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of sev

Strategic decision-making on low-carbon technology and network capacity investments using game theory

In recent years, renewable energy technologies have been increasingly adopted and seen as key to humanity’s efforts to reduce greenhouse gases emissions and combat climate change. Yet, a side effect is that renewables have reached high penetration rates in many areas, leading to undesired curtailment, especially if existing grid infrastructure is insufficient and renewable energy generated cannot be exported at areas of high energy demand. The issue of curtailment is compelling at remote areas, where renewable resources are abundant, such as in windy islands. Not only renewable production is wasted, but often curtailment comes with high costs for renewable energy developers and energy end-users. In fact, procedures on how generators access the grid and how curtailment is applied, are key factors that affect the decisions of investors about generation and grid capacity installed. Part of this thesis studies the properties of widely used curtailment rules, applied in several countries including the UK, and their effect on strategic interactions between self-interested and profit-maximising low-carbon technology investors. The work develops a game-theoretic framework to study the effects of curtailment on the profitability of existing renewable projects and future developments. More specifically, work presented in this thesis determines the upper bounds of tolerable curtailment at a given location that allows for profitable investments. Moreover, the work studies the effect of various curtailment strategies on the capacity factor of renewable generators and the effects of renewable resource spatial correlation on the resulting curtailment. In fact, power network operators face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this context, this thesis shows that fairness and equal sharing of imposed curtailment among generators is important to achieve maximisation of the renewable generation capacity installed at a certain area. A new rule is proposed that minimises disruption and the number of curtailment events a generator needs to respond to, while achieving fair allocation of curtailment between generators of unequal ratings. While curtailment can be reduced by smart grid techniques, a long term solution is increasing the network capacity. Grid reinforcements, however, are expensive and costs weight to all energy consumers. For this reason, debate in the energy community has focused on ways to attract private investment in grid reinforcement. A key knowledge gap faced by regulators is how to incentivise such projects, that could prove beneficial, especially in cases where several distributed generators can use the same power line to access the main grid, against the payment of a transmission fee. This thesis develops methods from empirical and algorithmic game theory to provide solutions to this problem. Specifically, a two-location model is considered, where excess renewable generation and demand are not co-located, and where a private renewable investor constructs a power line, providing also access to other generators, against a charge for transmission. In other words, the privately developed line is shared among all generators, a principle known as ‘common access’ line rules. This formulation may be studied as a Stackelberg game between transmission and local generation capacity investors. Decisions on optimal (and interdependent) renewable capacities built by investors, affect the resulting curtailment and profitability of projects and can be determined in the equilibrium of the game. A first approach to study the behaviour of investors at the game equilibrium, assumed a simple model, based on average values of renewable production and demand over a larger time horizon. This assumption allowed for an initial examination of the Stackelberg game equilibrium, by achieving an analytical, closed-form solution of the equilibrium and the investigation of its properties for a wide range of cost parameters. Next, a refined model is developed, able to capture the stochastic nature of renewable production and variability of energy demand. A theoretical analysis of the game is presented along with an estimation of the equilibrium by utilisation of empirical game-theoretic techniques and production/demand data from a real network upgrade project in the UK. The proposed method is general, and can be applied to similar case studies, where there is excess of renewable generation capacity, and where sufficient data is available. In practice, however, available data may be erroneous or experience significant gaps. To deal with data issues, a method for generating time series data is developed, based on Gibbs sampling. This attains an iterative simulation analysis with different time series data as an input (Markov Chain Monte Carlo), thus achieving the exploration of the solution space for multiple future scenarios and leading to a reduction of the uncertainty with regards to the investment decisions taken. Energy storage can reduce curtailment or defer network upgrades. Hence, the last part of this thesis proposes a model consisted of a line investor, local generators and a third independent storage player, who can absorb renewable production, that would otherwise have been curtailed. The model estimates optimal transmission, generation and storage capacities for various financial parameters. The value of storage is determined by comparing the energy system operation with and without energy storage. All models proposed in this thesis, are validated and applied to a practical setting of a grid reinforcement project, in the UK, and a large dataset of real wind speed measurements and demand. In summary, the research work studies the interplay among self-interested and indepen dent low-carbon investors, at areas of excess renewable capacity with network constraints and high curtailment. The work proposes a mechanism for setting transmission charges that ensures that the transmission line gets built, but investors from the local community, can also benefit from investing in renewable energy and energy storage. Overall, the results of this work show how game-theoretic techniques can help energy system stakeholders to bridge the knowledge gap about setting optimal curtailment rules and determining appropriate transmission charges for privately developed network infrastructure.Engineering and Physical Sciences Research Council (EPSRC

Analysis of investment decision making in power systems under environmental regulations and uncertainties

The dissertation focuses on the study of environmental policies and their impacts on the power systems\u27 planning. It consists of three parts, each of which addresses a single problem on environmental policies and generation expansion planning. In the first part of the dissertation, I compared the cap-and-trade policy and various carbon tax policies in a single period under the generation expansion framework. The problem was modeled as a bilevel problem where the lower level competing generation companies maximized their own profits under the regulations of the upper level. The policies were compared via their effectiveness and efficiency. Effectiveness referred to a policy\u27s capability to control the amount of carbon emissions, and efficiency was measured with respect to five criteria: emissions price, renewable energy portfolio, total generation, total profit of generation companies and grid owner, and government revenue. In the second part, the model was extended to multi-period planning to gain better views into market dynamics. Cap-and-trade and four variations of carbon tax policies were integrated in a game-theory based generation expansion planning model to assess their impacts on new investments in renewable energy generation capacity. The most efficient tax policy and variations were obtained using inverse equilibrium models. The third part complemented the previous parts by conducting a realistic case study on the generation expansion planning under uncertainty. It studied the formulation and solution of investment decisions in new generation under the explicit representation of environmental policies and their associated uncertainty. A three-layer framework was proposed to study the investment decisions. The operations layer was used to represent the transmission physical flows under economic dispatch in the network; the assessment layer completed comprehensive assessment of candidate investment plans under uncertainty; the optimization layer was designed to compare the optimal investment decisions for the decision makers based on the optimization criteria. Our framework was tested on a realistic 240-bus WECC network, taking into account representative scenarios and investment plans

Applications of Optimization Under Uncertainty Methods on Power System Planning Problems

This dissertation consists of two published journal paper, both on transmission expansion planning, and a report on distribution network hardening. We first discuss our studies of two optimization criteria for the transmission planning problem with a simplified representation of load and the forecast generation investment additions within the robust optimization paradigm. The objective is to determine either the minimum of the maximum investment requirement or the maximum regret with all sources of uncertainty explicitly represented. In this way, transmission planners can determine optimal planning decisions that are robust against all sources of uncertainty. We use a two layer algorithm to solve the resulting trilevel optimization problems. We also construct a new robust transmission planning model that considers generation investment more realistically to improve the quantification and visualization of uncertainty and the impacts of environmental policies. With this model, we can explore the effect of uncertainty in both the size and the location of candidate generation additions. The corresponding algorithm we develop takes advantage of the structural characteristics of the model so as to obtain a computationally efficient methodology. The two robust optimization tools provide new capabilities to transmission planners for the development of strategies that explicitly account for various sources of uncertainty. We illustrate the application of the two optimization models and solution schemes on a set of representative case studies. These studies give a good idea of the usefulness of these tools and show their practical worth in the assessment of ``what if\u27\u27 cases. We compare the performance of the minimax cost approach and the minimax regret approach under different characterizations of uncertain parameters. In addition, we also present extensive numerical studies on an IEEE 118-bus test system and the WECC 240-bus system to illustrate the effectiveness of the proposed decision support methods. The case study results are particularly useful to understand the impacts of each individual investment plan on the power system\u27s overall transmission adequacy in meeting the demand of the trade with the power output units without violation of the physical limits of the grid. In the report on distribution network hardening, a two-stage stochastic optimization model is proposed. Transmission and distribution networks are essential infrastructures to modern society. In the United States alone, there are there are more than 200,000 miles of high voltage transmission lines and numerous distribution lines. The power network spans the whole country. Such vast networks are vulnerable to disruptions caused by natural disasters. Hardening of distribution lines could significantly reduce the impact of natural disasters on the operation of power systems. However, due to the limited budget, it is impossible to upgrade the whole power network. Thus, intelligent allocation of resources is crucial. Optimal allocation of limited budget between different hardening methods on different distribution lines is explored

The Economics of Renewable Electricity Market Integration. An Empirical and Model-Based Analysis of Regulatory Frameworks and their Impacts on the Power Market

As power systems increase in complexity due to higher shares of intermitting RES-E, so increase the requirements for power system modeling. This thesis shows empirically, with examples from Germany and Texas, that the increasing RES-E share strongly affects current power market operation. The markets further create price signals, which lead to system adaptations in the long-run. To get an estimate of the adaptation effects, 'The High Temporal Resolution Electricity Market Analysis Model' (THEA) has been developed. In a first application for the ERCOT market in Texas, particular model attributes are tested and compared to some complexity reducing approaches, i.e. the reduction of temporal resolution and the reduction of operational constraints. In both cases, the results show significant differences compared to the results when the full spectrum of THEA's capabilities is utilized. The ERCOT case study additionally shows that the adaptation to RES-E in an isolated, mainly thermal-based power system is quite severe. Market signals which underline this conclusion are the severely reduced value of wind energy, the increasing curtailment and the strong shift towards peak-oriented generating capacities. The second application of THEA models the German power market with its interconnected markets. This analysis increases the complexity significantly by modeling a well interconnected system, increasing the amount of different RES-E technologies and adding CAES investment options. In order to assess the impact on the different system component's supply, demand and grid infrastructure, specific measures are applied to compare several scenarios. Each scenario represents a policy option, which either reduces or increases the flexibility of the power system. The scenario comparisons capture the effects of a lower RES-E share, a larger baseload capacity fleet, higher interconnector capacities, various RES-E support scheme designs and the capability of RES-E to participate in the reserve power market. In general, the results show that if the flexibility of one system component is reduced, the flexibility values of other system components increase, which suggests a careful, integrated and long-term oriented policy setting

Insurance mechanisms for the reliability of electricity supply

In the context of rapid shifts in the energy supply mix and the onset of climate change, tail risk in power systems presents an emergent threat to system reliability. Flexible resources like load control, storage and distributed energy resources are potent tools to alleviate system strains during extreme events. However, market participants need appropriate economic incentives to exploit the value of such resources. While spot prices serve as robust indicators of real-time scarcity, a complex challenge lies in translating short-term signals to long-term investment decisions. This is especially pertinent in the context of markets marked by incompleteness, and agents with pronounced aversion to risk. The financial technology of insurance is targeted at the assessment, pricing, and management of extreme and catastrophic risks. This thesis proposes the novel application of insurance contracts and risk architectures to modern electricity markets, extending existing approaches to reliability risk management. This leads to the central research question of this thesis: Can the delivery of electricity service to consumers be made more reliable through the application of insurance mechanisms? The thesis investigates this question through three main streams of research: This first stream proposes the novel application of insurance contracts and capital reserving frameworks on the procurement of strategic reserves in electricity markets. A strategic reserve is a reliability mechanism in electricity markets that seeks to contract generation capacity incremental to that incentivised by short-term spot markets, for use in times of critical supply shortage. The insurance contracts allow consumers to elect differentiated reliability preferences, and align the financial interests of the insurer with such preferences. Application to a case study suggests the potential for improved consumer and social welfare while maintaining insurer viability and solvency. The design is also robust to non-transparent market parameters such as generator risk aversion. The second stream develops a locational insurance model to value resilience in power systems exposed to high-impact low-probability common-mode events. It is demonstrated that the implementation of this scheme in a large-scale power system could reduce load losses via investment in resilient distributed energy resources. However the cost of such insurance may be expensive, and appropriate calibration of consumer expectations and preferences is important. The final stream examines the interaction between the design of contracts between central agencies and storage resources, and the operation of the resources in the market. Five principles for central agency contracting are proposed, focusing on incentive compatibility with existing spot dispatch and limiting distortions to long-term hedging markets. The principles are applied specifically to contracts with storage resources. It is demonstrated that many early designs for storage auctions may be inconsistent with the identified principles. A novel storage contract ‘yardstick’ is proposed, which is shown to align participant dispatch incentives, while maintaining revenue support

A review on economic and technical operation of active distribution systems

© 2019 Elsevier Ltd Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods

Market Design for the Transition to Renewable Electricity Systems

The research carried out in this thesis aims to shed light on the role of the European electricity market design in the transition to a target electricity system that combines sustainability, affordability, and reliability. While the ongoing expansion of fluctuating renewable electricity sources challenges the established structures and market mechanisms, governments across Europe have decided to phase-out certain conventional technologies like coal or nuclear power. Since traditional electricity systems rely on flexibility provided by controllable generation capacity, other flexibility options are needed to compensate for the decommissioned conventional power plants and support the system integration of renewables. Against this background, the dissertation extends an established large-scale agent-based electricity market model in order to account for the developments towards an integrated European electricity market and the characteristics of storage technologies. In particular, the representation of cross-border effects is enhanced by integrating approaches from the fields of operations research, non-cooperative game theory, and artificial intelligence in the simulation framework. The extended model is then applied in three case studies to analyze the diffusion of different flexibility options under varying regulatory settings. These case studies cover some central aspects of the European electricity market, most importantly capacity remuneration mechanisms, the interaction of day-ahead market and congestion management, and the role of regulation for residential self-consumption. Results of the case studies confirm that by designing the regulatory framework, policymakers and regulators can substantially affect quantity, composition, location, and operation of technologies – both, on the supply side and the demand side. At the same time, changes and amendments to market design are frequent and will continue to be so in the years ahead. Moreover, given the increasing level of market integration in Europe, the role of cross-border effects of national market designs will gain further in importance. In this context, agent-based simulation models are a valuable tool to better understand potential long-term effects of market designs in the interconnected European electricity system and can therefore support the European energy transition

Transmission and interconnection planning in power systems: Contributions to investment under uncertainty and cross-border cost allocation.

<p>Electricity transmission network investments are playing a key role in the integration process of power systems in the European Union. Given the magnitude of investment costs, their irreversibility, and their impact in the overall development of a region, accounting for the role of uncertainties as well as the involvement of multiple parties in the decision process allows for improved and more robust investment decisions. Even though the creation of this internal energy market requires attention to flexibility and strategic decision-making, existing literature and practitioners have not given proper attention to these topics. Using portfolios of real options, we present two stochastic mixed integer linear programming models for transmission network expansion planning. We study the importance of explicitly addressing uncertainties, the option to postpone decisions and other sources of flexibility in the design of transmission networks. In a case study based on the Azores archipelago we show how renewables penetration can increase by introducing contingency planning into the decision process considering generation capacity uncertainty. We also present a two-party Nash-Coase bargaining transmission capacity investment model. We illustrate optimal fair share cost allocation policies with a case study based on the Iberian market. Lastly, we develop a new model that considers both interconnection expansion planning under uncertainty and cross-border cost allocation based on portfolios of real options and Nash-Coase bargaining. The model is illustrated using Iberian transmission and market data.</p