We discuss a model of a virtual power plant (VPP) that provides market access to privately-owned distributed generations (DGs). The VPP serves passive loads, processes bids from generators, and trades in the wholesale market. The generators can be renewable or thermal, and they act strategically to maximize their own profit. The VPP establishes the rules of the internal market to minimize the cost of energy and the cost of balancing while ensuring generator participation and load balancing. We derive a heuristic mechanism for internal market and propose a dynamic programming approach for minimizing the VPP cost. We present illustrative simulations for both single and multistage market bidding and then compare the resulting performance to the centralized VPP model, where the DGs are assumed to be owned by the VPP. We show that the proposed design incentivizes the DG agents to behave the same as in the centralized case, but the optimal cost paid by VPP is higher due to the payments to the DG owners.

Among renewable energy resources, wind power is poised to contribute most significantly to meeting future wholesale electricity demand. However, the intermittent nature of wind power makes maintaining system reliability a challenge as the share of installed wind capacity on the grid increases. In New England, wind plants are currently unable to receive automatic dispatch instructions from the regional grid operator, but a centralized wind forecasting system under development will enable wind plants to be dispatched by ISO New England’s automatic dispatch software by 2016. Wind plants will receive an upper bound to their production through so-called Do Not Exceed (DNE) dispatch limits. This study evaluates how the automatic dispatch of wind plants in the ISO New England control area will impact wind plant output, emissions, wholesale energy market prices, and the system-wide generation mix. Wind generation is modeled using 10-minute time-series wind speed data from the National Renewable Energy Laboratory’s Eastern Wind Dataset. Market outcomes for 2020 are then simulated using the spreadsheet-based Oak Ridge Competitive Electricity Dispatch (ORCED) model which mimics the economic dispatch of power plants in deregulated wholesale electricity markets. Results show that imposing DNE dispatch limits reduce total wind generation by a small amount – 6.47% over the course of the study year. The study finds that DNE dispatch limits constrain wind generation often – 28.4% of the year on average – but that the levels of wind generation avoided were typically small – 72.4% of DNE limit curtailment events were at levels below 5% of plant nameplate capacity.

Game theory has its applications in various domains, but has only recently been applied to study open problems in smart microgrids. A simple microgrid system with a small wind farm, a storage facility and an aggregate load entity is studied here using a non-cooperative game-theoretic framework. The framework developed is used to study the behavior of rational market participants (players), namely wind power producer and energy storage. The framework is implemented to find the existence of any Nash equilibria and see if cooperation is a natural outcome of the game. If cooperation is not self-enforcing then usefulness of the framework to find the conditions for cooperation is presented. It must be noted that cooperation is not automatically guaranteed as the payoff of the energy storage operator is dependent on the strategy employed by the wind power producer. Similarly, the payoff for the wind power producer is highly intertwined with the strategy employed by the energy storage operator. Historical weather and market data is used to calculate expected payoffs for each possible combination of strategies. The results are presented in the form of payoff matrices and the best response algorithm and/or elimination of dominated strategies is used to find the Nash equilibrium. Sensitivity of the Nash equilibrium to various storage parameters like storage size, charging/discharging limits, charging/discharging efficiency, and other market parameters like energy imbalance penalties, efficiency of up/down regulation, and electricity market prices is studied and necessary conditions for cooperation are presented. / Master of Science

Determining the maximum power system loadability, as well as preventing the system from being operated close to the stability limits is very important in power systems planning and operation. The application of optimization techniques to power systems security and electricity markets is a rather relevant research area in power engineering. The study of optimization models to determine critical operating conditions of a power system to obtain secure power dispatches in an electricity market has gained particular attention. This thesis studies and develops optimization models and techniques to detect or avoid voltage instability points in a power system in the context of a competitive electricity market. A thorough analysis of an optimization model to determine the maximum power loadability points is first presented, demonstrating that a solution of this model corresponds to either Saddle-node Bifurcation (SNB) or Limit-induced Bifurcation (LIB) points of a power flow model. The analysis consists of showing that the transversality conditions that characterize these bifurcations can be derived from the optimality conditions at the solution of the optimization model. The study also includes a numerical comparison between the optimization and a continuation power flow method to show that these techniques converge to the same maximum loading point. It is shown that the optimization method is a very versatile technique to determine the maximum loading point, since it can be readily implemented and solved. Furthermore, this model is very flexible, as it can be reformulated to optimize different system parameters so that the loading margin is maximized. The Optimal Power Flow (OPF) problem with voltage stability (VS) constraints is a highly nonlinear optimization problem which demands robust and efficient solution techniques. Furthermore, the proper formulation of the VS constraints plays a significant role not only from the practical point of view, but also from the market/system perspective. Thus, a novel and practical OPF-based auction model is proposed that includes a VS constraint based on the singular value decomposition (SVD) of the power flow Jacobian. The newly developed model is tested using realistic systems of up to 1211 buses to demonstrate its practical application. The results show that the proposed model better represents power system security in the OPF and yields better market signals. Furthermore, the corresponding solution technique outperforms previous approaches for the same problem. Other solution techniques for this OPF problem are also investigated. One makes use of a cutting planes (CP) technique to handle the VS constraint using a primal-dual Interior-point Method (IPM) scheme. Another tries to reformulate the OPF and VS constraint as a semidefinite programming (SDP) problem, since SDP has proven to work well for certain power system optimization problems; however, it is demonstrated that this technique cannot be used to solve this particular optimization problem.

Determining the maximum power system loadability, as well as preventing the system from being operated close to the stability limits is very important in power systems planning and operation. The application of optimization techniques to power systems security and electricity markets is a rather relevant research area in power engineering. The study of optimization models to determine critical operating conditions of a power system to obtain secure power dispatches in an electricity market has gained particular attention. This thesis studies and develops optimization models and techniques to detect or avoid voltage instability points in a power system in the context of a competitive electricity market. A thorough analysis of an optimization model to determine the maximum power loadability points is first presented, demonstrating that a solution of this model corresponds to either Saddle-node Bifurcation (SNB) or Limit-induced Bifurcation (LIB) points of a power flow model. The analysis consists of showing that the transversality conditions that characterize these bifurcations can be derived from the optimality conditions at the solution of the optimization model. The study also includes a numerical comparison between the optimization and a continuation power flow method to show that these techniques converge to the same maximum loading point. It is shown that the optimization method is a very versatile technique to determine the maximum loading point, since it can be readily implemented and solved. Furthermore, this model is very flexible, as it can be reformulated to optimize different system parameters so that the loading margin is maximized. The Optimal Power Flow (OPF) problem with voltage stability (VS) constraints is a highly nonlinear optimization problem which demands robust and efficient solution techniques. Furthermore, the proper formulation of the VS constraints plays a significant role not only from the practical point of view, but also from the market/system perspective. Thus, a novel and practical OPF-based auction model is proposed that includes a VS constraint based on the singular value decomposition (SVD) of the power flow Jacobian. The newly developed model is tested using realistic systems of up to 1211 buses to demonstrate its practical application. The results show that the proposed model better represents power system security in the OPF and yields better market signals. Furthermore, the corresponding solution technique outperforms previous approaches for the same problem. Other solution techniques for this OPF problem are also investigated. One makes use of a cutting planes (CP) technique to handle the VS constraint using a primal-dual Interior-point Method (IPM) scheme. Another tries to reformulate the OPF and VS constraint as a semidefinite programming (SDP) problem, since SDP has proven to work well for certain power system optimization problems; however, it is demonstrated that this technique cannot be used to solve this particular optimization problem.

One of the main concerns with the introduction of competition in the power sector is the strategic behaviour of market participants. Computable models of strategic behaviour are becoming increasingly important to understand the complexities of competition. Such models can help analyze market designs and regulatory policies. In this thesis, further developments on the modelling and analysis of strategic behaviour in electricity markets are presented. This thesis work has been conducted along three research lines. <br /><br /> In the first research line, an oligopolistic model of a joint energy and spinning reserve market is formulated to analyze imperfect competition. Strategic behaviour is introduced by means of conjectured functions. With this integrated formulation for imperfect competition, the opportunity cost between generation and spinning reserve has been analytically derived. Besides, inter-temporal and energy constraints, and financial transmission rights are taken into account. Under such considerations, competition in electricity markets is modelled with more realism. The oligopolistic model is formulated as an equilibrium problem in terms of complementarity conditions. <br /><br /> In the second research line, a methodology to screen and mitigate the potential exacerbation of market power due to the ownership of financial transmission rights is presented. Hedging position ratios are computed to quantify the hedging level of financial transmission rights. They are based on the actual impact that each participant has in the energy market, and on the potential impact that it would have with the ownership of financial transmission rights. Thus, hedging position ratios are used to identify the potential gambling positions from the transmission rights bidders, and, therefore, used to prioritize critical positions in the auction for transmission rights. <br ><br /> In the last research line, alternative equilibrium models of markets for financial transmission rights are formulated. The proposed equilibrium framework is more natural and flexible for modelling markets than the classic cost-minimization markets. Different markets for financial transmission rights are modelled, namely: i) forwards, ii) options, and iii) joint forwards and options. Moreover, one-period, multi-period and multi-round markets for forwards are derived. These equilibrium models are proposed to analyze the bidding strategies of market participants. The potential impact of bidders on congestion prices is modelled by means of conjectured transmission price functions.

Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program. In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007. Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid technique considerably improves the electricity price forecasting.

Cette thèse porte sur une série de problèmes d’efficacité économique créés par le développement de masse en Europe des sources d’énergie renouvelable (SER) intermittentes. Les ressources flexibles requises pour compenser certaines de leurs propriétés (variabilité, faible prévisibilité, sites spécifiques) sont connues, mais des signaux sont nécessaires pour en assurer un développement et des opérations efficaces. Une première question qui se pose est de savoir dans quelle mesure les SER intermittentes peuvent rester en dehors des marchés de l’électricité, alors qu’elles y jouent un rôle-clé. Une seconde question est de déterminer dans quelle mesure le design de marché actuel est adapté à un système électrique contenant une part très importante de SER intermittentes. Ces deux questions sont traitées ici dans quatre contributions indépendantes.Le premier chapitre consiste en une revue critique de littérature. On y introduit et compare deux paradigmes (qui sont souvent implicites) pour l’intégration des SER. On identifie ensuite des évolutions requises afin d’adapter le design de marché au développement de SER intermittentes, telles qu’une redéfinition des produits échangés, ou une réorganisation de la séquence de marchés.On emploie dans le deuxième chapitre un modèle analytique, afin d’évaluer le potentiel des marchés infra-journaliers pour gérer la faible-prévisibilité des SER intermittentes. Cette étude démontre comment ce potentiel est en grande partie déterminé par l’évolution des erreurs de prévision.Le troisième chapitre s’intéresse aux bénéfices qui peuvent résulter d’une restriction de la production des SER intermittentes à un instant donné. Un autre modèle analytique est utilisé pour décortiquer l’influence de paramètres clés sur ces bénéfices. Une attention particulière est portée à la distribution de ces bénéfices entre les différents acteurs.Enfin on réalise dans le chapitre 4 une simulation numérique de l’évolution des bilans des gestionnaires du réseau de transport (GRTs) européens, afin de souligner les difficultés posées par la connexion des SER intermittentes au réseau de transport. Des stratégies de financement alternatives sont également évaluées. Cette étude révèle l’existence d’un déficit de financement important en cas d’évolution constante des tarifs. / This thesis centres on issues of economic efficiency originating from the large-scale development of intermittent renewable energy sources (RES) in Europe. The flexible resources that are necessary to cope with their specificities (variability, low-predictability, site specificity) are already known, but adequate signals are required to foster efficient operation and investment in these resources. A first question is to what extent intermittent RES can remain out of the market at times when they are the main driver of investment and operation in power systems. A second question is whether the current market design is adapted to their specificities. These two questions are tackled in four distinct contributions.The first chapter is a critical literature review. This analysis introduces and confronts two (often implicit) paradigms for RES integration. It then identifies and discusses a set of evolutions required to develop a market design adapted to the large-scale development of RES, such as new definitions of the products exchanged and reorganisation of the sequence of electricity markets.In the second chapter, an analytical model is used to assess the potential of intraday markets as a flexibility provider to intermittent RES with low production predictability. This study highlights and demonstrates how the potential of intraday markets is heavily dependent on the evolution of the forecast errors.The third chapter focuses on the benefits of curtailing the production by intermittent RES, as a tool to smooth out their variability and reduce overall generation costs. Another analytical model is employed to anatomize the relationship between these benefits and a set of pivotal parameters. Special attention is also paid to the allocation of these benefits between the different stakeholders.In the fourth chapter, a numerical simulation is used to evaluate the ability of the European transmission system operators to tackle the investment wave required in order to manage the production of intermittent RES. Alternative financing strategies are then assessed. The findings reveal that under the current trend of tariffs, the volumes of investment forecasted will be highly challenging for transmission system operators.

Participants in deregulated electric power markets compete for financial transmission rights (FTRs) to hedge against losses due to transmission congestion by submitting bids to the independent system operator (ISO). The ISO obtains an FTR allocation, that maximizes sales revenue while satisfying simultaneous feasibility. This FTR allocation remains in place for a length of time during which the participants compete in the energy market to maximize their total payoff from both FTR and energy markets. Energy markets (bi-lateral, day ahead, real time) continue until the the end of the current FTR period, at which time the participants can choose to modify their FTR holdings for the next FTR period. As in any noncooperative game, finding Nash equilibrium bidding strategies is of critical importance to the participants in both FTR and energy markets. In this research, a two-tier matrix game theoretic modeling approach is developed that can be used to obtain equilibrium bidding behavior of the participants in both FTR and energy markets considering the total payoff from FTR and energy. The matrix game model presents a significant deviation from the bilevel optimization approach commonly used to model FTR and energy allocation problems. A reinforcement learning (RL) algorithm is also developed which uses a simulation model and a value maximization approach to obtain the equilibrium bidding strategies in each market. The model and the RL based solution approach allow consideration of multi-dimensional bids (for both FTR and energy markets), network contingencies, varying demands, and many participants. The value iteration based RL algorithm obtains pure strategy Nash equilibrium for FTR and energy allocation. A sample network with three buses and four participants is considered for demonstrating the viability of the game theoretic model for FTR market. A PJM network example with five buses, five generators and three loads is also considered to analyze equilibrium bidding behavior in joint FTR and energy markets. Several numerical experiments on the sample networks are conducted using the approach of statistical design of experiments (DOE) to assess impacts of variations of bid and network parameters on the market outcomes like participant payoffs and equilibrium strategies.

This work deals with problems where multiple actors simultaneously take control decisions and implement the corresponding actions in large multi-area power systems. The fact that those actions take place in the same transmission grid introduces a coupling between the various decision-making problems. First, transmission constraints involving all actors' controls must be satisfied, while, second, the satisfaction of an actor's operational objective depends, in general, not only on its own actions but on the others' too. Algorithms and/or operational procedures are, thus, developed seeking to reconcile the multiple actors' simultaneous decisions. The confidentiality and operational autonomy of the actors' decision-making procedures are preserved. In particular, two specific problems leading to such a multi-actor situation have been treated. The first is drawn from a recently emerging situation, at least in Europe, where several Transmission System Operators (TSOs) have installed and/or are planning to install Phase Shifting Transformers (PSTs) in such locations in their areas that, by properly adjusting the PST phase angle settings, they can significantly control the power flows entering and exiting their systems. A general framework is proposed for the control of PSTs owned by several TSOs, taking into account their interactions. The proposed solution is the Nash equilibrium of a sequence of optimizations performed by the various TSOs, each of them taking into account the other TSOs' control settings as well as operating constraints relative to the whole system. The method is applied to a linearized network model and illustrated on the IEEE 118-bus system. The second multi-actor situation dealt with in this work stems from the recently increasing amount of discussions and efforts made towards creating the right market structures and operational practices that would facilitate a seamless inter-area trade of electricity throughout large interconnections. In this respect, in accordance with European Union's goal of a fully functional Internal Electricity Market where ideally every consumer will be able to buy electric energy from every producer all across the interconnection, the possibility of every market participant to place its bid in whatever electricity market of an interconnection has been considered. This results in overlapping markets, each with its own schedule of power injections and withdraws, comprising buses all around the interconnection, that are cleared simultaneously by Transaction Schedulers (TSs). An iterative procedure is proposed to reconcile the various TS schedules such that congestion is managed in a fair and efficient way. The procedure converges to such schedules that the various TS market clearings are in a Nash equilibrium. The method is then extended towards several directions: enabling market participants to place their bids simultaneously in more than one TS's market, incorporating $N-1$ security constraints, allowing for joint energy-reserve dispatch, and, accounting for transmission losses. The corresponding iterative algorithms are thoroughly illustrated in detail on a 15-bus as well as the IEEE RTS-96 system.