Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings

Short-term electric market dynamics and generation company decision-making in new deregulated environment

Under the framework developed in [Sheble, 1999a], this work simulates electric market dynamic using systems theory, decision analysis and decision theory. Activities of Generation Companies (GENCOs), the most active players in electric markets, and their impact on market performances are also examined. Decision-making of GENCOs and interactions between them are studied using decision analysis and decision theory.;The first part of this study studies electric market dynamics: dynamics of electricity price, generation output, and other variables. The problem is examined from the viewpoint of an Independent Contract Administrator (ICA) to simulate market performance and GENCOs\u27 activities in different situations. These situations include various interactions among GENCOs (different expectations for competitors adopted by GENCOs), competition types (quantity competition, price competition, both price and quantity competition), market risk levels (decisions under certainty and uncertainty), and different market organizations (with and without certain market information feedback) in the electric market. Decision-making of GENCOs and interactions between them are modeled as control processes and electric markets are modeled as control systems. The corresponding market dynamics is simulated and market dynamic properties are obtained. Simulation results show that interactions between market participants, as well as market risk levels, competition types, and market organizations, are important to market participant\u27s activities and have significant impact on market performances and properties.;The second part of this study is from GENCOs\u27 viewpoint to develop optimal decision-making strategies and models in short term. First of all, GENCOs decision problem in short term in new deregulated environment is identified as a three-dimension problem: how to make optimal decisions for different time in different geographical markets in different service markets to maximize total gain. Then, a new market-based generation scheduling scheme is proposed to solve this problem. Market rules, competitor\u27s activities, uncertainty in the market, bidding strategies, and short-term generation technical constraints are included in the scheme and analyzed using decision analysis and decision theory. Next, Dynamic Programming (DP) and Stochastic Dynamic Programming (SDP) are adopted to solve the new scheduling problems. Results show that in new environment, GENCOs\u27 optimal generation schedules may be very different from schedules proposed in previous work. (Abstract shortened by UMI.

Strategic bidding in an energy brokerage

The main contribution of this research is the definition, and the demonstration of use, of a framework for the development and evaluation of bidding strategies, for participants to use, in preparing and submitting bids to an energy brokerage market. The framework includes the rules under which the market operates, the different types of participants and their objectives, the factors that affect the bidding of the participants, strategies that consider these factors and achieve the objectives, and a simulator to simulate market conditions, including competition from other participants, with which to test these strategies;Strategies that attempt to include competitor behavior by using available market information are developed. A lower bound on the profit from bidding is derived, which is useful in providing an objective function that can be optimized using the limited information assumed to be available in this research. This is followed by derivations for optimal bids that maximize this lower bound, for different assumptions about the probability distribution of the competitors;The simulator is expected to be helpful in testing of the strategies. However, the strategies will be independent of the simulator, and will be applicable to participants who choose a different (presumably more advanced) tool for evaluation. The contribution of this research includes original ways to utilize the information generated by the simulator;Some of the results of the simulations performed using this simulator to test the strategies developed are presented and analyzed. Also, based on these results, some heuristics were developed to improve the performance of the strategies. Results from implementing these heuristics are also presented;A qualitative treatment of the scheduling factors that might affect bidding strategies is presented, followed by numerical examples to illustrate the effects. A treatment of risk preferences by using results from recent developments in utility theory and risk preference functions by researchers in economics, is presented. This is followed by the modeling of bidding objectives as expected utility maximizations, and the comparison of results from using this type of objective to using the expected profit maximization objective for various scheduling scenarios

Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term Scheduling

Current technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.A tecnologia atual na monitorização inteligente, incluindo a Internet of Things (IoT), permite que a rede elétrica ao nível da transporte e distribuição faça uso de programas de demand response (DR) para garantir a operação segura e económica dos sistemas de energia. A liberalização e a reestruturação da indústria dos sistemas de energia elétrica também promovem a gestão do lado da procura de forma otimizada. Os impactes da implementação de DR no mercado elétrico podem ser expressos pelo conceito de agregadores de DR (DRAs), sendo a interface entre o lado da oferta e o lado da procura de energia elétrica. Vários mercados, como os mercados diário e em tempo real, são estudados visando a gestão otimizada do ponto de vista do Independent System Operator (ISO) ou do Distribution System Operator (DSO). Para atingir os objetivos propostos, modelos de otimização em um ou dois níveis podem ser desenvolvidos. O comportamento das fontes de energia renováveis dependentes do clima, como a produção de energia eólica e fotovoltaica que acarretam incerteza, é modelado pelo método de simulação de Monte Carlo. Ainda, two-stage stochastic programming é aplicada para minimizar o custo de operação. Os resultados deste estudo demonstram a importância de considerar todos os participantes efetivos no mercado, como DRAs e clientes finais, no custo de operação. Ainda, considerando a incerteza no modelo beneficia os operadores da rede na redução de custos, capacitando a resiliência e fiabilidade da rede

Optimal management of demand response aggregators considering customers' preferences within distribution networks

In this paper, a privacy-based demand response (DR) trading scheme among end-users and DR aggregators (DRAs) is proposed within the retail market framework and by Distribution Platform Optimizer (DPO). This scheme aims to obtain the optimum DR volume to be exchanged while considering both DRAs’ and customers’ preferences. A bilevel programming model is formulated in a day-ahead market within retail markets. In the upper-level problem, the total operation cost of the distribution system, which consists of DRAs’ cost and other electricity trading costs, is minimized. The production volatility of renewable energy resources is also taken into account in this level through stochastic two-stage programming and MonteCarlo Simulation method. In the lower-level problem, the electricity bill for customers is minimized for customers. The income from DR selling is maximized based on DR prices through secure communication of household energy management systems (HEMS) and DRA. To solve this convex and continuous bilevel problem, it is converted to an equivalent single-level problem by adding primal and dual constraints of lower level as well as its strong duality condition to the upper-level problem. The results demonstrate the effectiveness of different DR prices and different number of DRAs on hourly DR volume, hourly DR cost and power exchange between the studied network and the upstream network.©2020 The Institution of Engineering and Technology. This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission and Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Control of Energy Storage

Energy storage can provide numerous beneficial services and cost savings within the electricity grid, especially when facing future challenges like renewable and electric vehicle (EV) integration. Public bodies, private companies and individuals are deploying storage facilities for several purposes, including arbitrage, grid support, renewable generation, and demand-side management. Storage deployment can therefore yield benefits like reduced frequency fluctuation, better asset utilisation and more predictable power profiles. Such uses of energy storage can reduce the cost of energy, reduce the strain on the grid, reduce the environmental impact of energy use, and prepare the network for future challenges. This Special Issue of Energies explore the latest developments in the control of energy storage in support of the wider energy network, and focus on the control of storage rather than the storage technology itself

Valuation and investment of generation assets

The re-regulation of electric power industry around the world has raised many new challenges for all stakeholders. This research is to valuate generation assets within re-regulated electricity markets, both in short-term and long-term. The focus is to valuate operation flexibility under market uncertainties from the viewpoint of a Generation Company (GENCO);This research proposes to model the movements of electricity markets with Hidden Markov Model (HMM) driven by underlying market forces. An electricity market is modeled as a dynamic system evolving over time according to Markov processes. At any time interval, the electricity market can be in one state and transit to another state in the next time interval. The true market states are hidden from a market participant behind the incomplete observation. The observations, such as market-clearing price and quantity, are modeled to follow multiple probabilistic distributions;This research proposes to further decompose the market forces into physical and economic drivers if a specific electricity market employs Location Marginal Price (LMP) mechanism. The physical drivers include transmission network topology and generation technology. The economic drivers include fuel prices, demand uncertainties, and profit maximization of market participants with incomplete information. The decomposition captures the strengths of engineering-based production cost approach and mark-to-market stochastic approach;This research valuates generation assets with real option analysis. The value of generation assets is maximized based on the Hidden Markov Model (HMM) and newest observation of electricity markets. Such an optimization problem is formulated as Partially Oberserable Markov Decision Problem (POMDP). The solution of a POMDP provides a GENCO both the optimal operating policy and values of generation assets. The value of perfect and imperfect information is also identified;Investment in generation assets is also analyzed with real option. This research incorporates fuzzy sets and numbers to capture the fuzziness and possibilities of long-term electricity markets movements. Fuzzy sets and numbers provide the modeler flexibilities to incorporate subjective judgments when rigorous approaches are not feasible. The real call options, capturing the investment value of generation assets, are formulated as Markov Decision Process (MDP) and solved with fuzzy linear programming

Offtake Strategy Design for Wind Energy Projects under Uncertainty

Energy use from wind, solar, and other renewable sources is a public policy at the federal and state levels to address environment, energy, and sustainability concerns. As the cost of renewable energy is still relatively high compared to fossil fuels, it remains a critical challenge to make renewable energy cost competitive, without relying on public subsidies. During recent years, much advance has been made in our understanding of technology innovations and cost structure optimization of renewable energy. A knowledge gap exists on the other side of the equation - revenue generation. Considering the complexity and stochastic nature of renewable energy projects, there is great potential to optimize the revenue generation mechanisms in a systematic fashion for improved profitability and growth. This dissertation examines two primary revenue generation mechanisms, or offtake strategies, used in wind energy development projects in the U.S. While a short-term offtake strategy allows project developers to benefit from price volatility in the wholesale spot market for profit maximization, a long-term offtake strategy minimizes the market risk exposure through a long-term Power Purchase Agreement (PPA). With Conditional Value-at-Risk (CVaR) introduced as a risk measure, this dissertation first develops two stochastic programming models for optimizing offtake designs under short and long-term strategies respectively. Furthermore, this study also proposes a hybrid offtake strategy that combines both short and long-term strategies. The two-level stochastic model demonstrates the merit of the hybrid strategy, i.e. obtaining the maximized profit while maintaining the flexibility of balancing and hedging against market and resource risks efficiently. The Cape Wind project in Massachusetts has been used as an example to demonstrate the model validity and potential applications in optimizing its revenue streams. The analysis shows valuable implications on the optimal design of renewable energy project development in regard to offtake arrangements

Bi-Level Optimization Considering Uncertainties of Wind Power and Demand Response

Recently, world-wide power systems have been undergone a paradigm change with increasing penetration of renewable energy. The renewable energy is clean with low operation cost while subject to significant variability and uncertainty. Therefore, integration of renewables presents various challenges in power systems. Meanwhile, to offset the uncertainty from renewables, demand response (DR) has gained considerable research interests because of DR’s flexibility to mitigate the uncertainty from renewables. In this dissertation, various power system problems using bi-level optimization are investigated considering the uncertainties from wind power and demand response. In power system planning, reactive power planning (RPP) under high-penetration wind power is studied in this dissertation. To properly model wind power uncertainty, a multi-scenario framework based on alternating current optimal power flow (ACOPF) considering the voltage stability constraint under the worst wind scenario and transmission N-1 contingency is developed. The objective of RPP in this work is to minimize the VAR investment and the expected generation cost. Benders decomposition is used to solve this model with an upper level problem for VAR allocation optimization and generation cost minimization as a lower problem. Then, several problems related wind power and demand response uncertainties under power market operation are investigated. These include: an efficient and effective method to calculate the LMP intervals under wind uncertainty is proposed; the load serving entities’ strategic bidding through a coupon-based demand response (CBDR) with which a load serving entity (LSE) may participate in the electricity market as strategic bidders by offering CBDR programs to customers; the impact of financial transmission right (FTR) with CBDR programs is also studied from the perspective of LSEs; and the stragegic scheduling of energy storages owned by LSEs considering the impact of charging and discharging on the bus LMP. In these problems, a bi-level optimization framework is presented with various objective functions representing different problems as the upper level problems and the ISO’s economic dispatch (ED) as the lower level problem. The bi-level model is addressed with mathematic program with equilibrium constraints (MPEC) model and mixed-integer linear programming (MILP), which can be easily solved with the available optimization software tool