We consider a smart-grid connecting several agents, modeled as stochastic dynamical systems, who may be electricity consumers producers. At each discrete time instant, which may represent a 15 minute interval, each agent may consume generate some quantity of electrical energy. The Independent System Operator (ISO) is given the task of assigning consumptions generations to the agents so as to maximize the sum of the utilities accrued to the agents, subject to the constraint that energy generation equals consumption at each time. This task of coordinating generation and demand has to be accomplished by the ISO without the agents revealing their system states, dynamics, or utility cost functions. We show how and when a simple iterative procedure converges to the optimal solution. The ISO iteratively obtains electricity bids by the agents, and declares the tentative market clearing prices. In response to these prices, the agents submit new bids. On the demand side, the solution yields an optimal demand response for dynamic and stochastic loads. On the generation side, it provides the optimal utilization of stochastically varying renewables such as solar wind, and generation with fossil fuel based generation with dynamic constraints such as ramping rates. Thereby we solve a decentralized stochastic control problem, without agents sharing any information about their system models, states or utility functions.
In the setting of the energy market, @cite_0 @cite_8 discuss a general framework for the operation of an electicity grid via the establishment of a marketplace in which the electricity purchases are done on the basis of spot prices. Thus, electrical energy is treated as a commodity which can be bought, sold, and traded, taking into account its time-and space-varying values and costs, and a framework is presented for the establishment of an energy marketplace. Our results can be viewed as providing guidelines for the operation of this marketplace via simple bidding schemes when the agents are nonlinear stochastic dynamical systems. Our scheme maximize the net social utlity.
@cite_0 @cite_8
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This paper proposes a decentralised and privacy-preserving local electricity trading market. The market employs a bidding protocol based on secure multiparty computation and allows users to trade their excess electricity among themselves. The bid selection and trading price calculation are performed in a decentralised and privacy-preserving manner. We implemented the market in C++ and tested its performance with realistic data sets. Our simulation results show that the market tasks can be performed for 2500 bids in less than four minutes in the “online” phase, showing its feasibility for a typical electricity trading period. Integration of distributed generation based on renewable energy sources into the power system has gained popularity in recent years. Many small-scale electricity suppliers (SESs) have recently entered the electricity market, which has been traditionally dominated by a few large-scale electricity suppliers. The emergence of SESs enables direct trading (DT) of electricity between SESs and end-users (EUs), without going through retailers, and promotes the possibility of improving the benefits to both parties. In this paper, the cooperation between SESs and EUs in DT is analyzed based on coalitional game theory. In particular, an electricity pricing scheme that achieves a fair division of revenue between SESs and EUs is analytically derived by using the asymptotic Shapley value. The asymptotic Shapley value is shown to be in the core of the coalitional game such that no group of SESs and EUs has an incentive to abandon the coalition, which implies the stable operation of DT for the proposed pricing scheme. Unlike the existing pricing schemes that typically require multiple stages of calculations and real time information about each participant, the electricity price for the proposed scheme can be determined instantaneously based on the number of participants in DT and statistical information about electricity supply and demand. Therefore, the proposed pricing scheme is suitable for practical implementation. Using computer simulations, the price of electricity for the proposed DT scheme is examined in various environments, and the numerical results validate the asymptotic analysis. Moreover, the revenues of the SESs and EUs are evaluated for various types of SESs and different numbers of participants in DT. The optimal ratio of different types of SESs is also investigated. The trading is a well known, easy and efficient method of distributing goods. Energy cannot be stored efficiently, which implies that supply and demand of power has to be balanced at all times; this adds complexity to the trading of electric power. Introduction of microgrids, with internal balancing of power and trading with external power grid, brings many questions about power distribution, sharing of costs and revenues. Using market mechanism for such purpose seems appropriate. In this article we present an example of balancing a microgrid with its own energy production sources and connected to a higher voltage distribution grid, by introduction of the continuous double auction market with agents following the Adaptive-Aggressive Strategy. A smart grid connects several electricity consumers producers, e.g., wind solar storage farms, fossil-fuel plants, industrial commercial loads, or load-serving aggregators, all modeled as stochastic dynamical systems. In each time period, each consumes supplies some electrical energy. Each such agent's utility is the benefit accrued from its consumption or the negative of its generation cost. The social welfare, the sum of all these utilities, is the total benefit accrued from all consumption minus the total cost of generation. The independent system operator is charged with maximizing the social welfare subject to total generation equalling consumption in each time period, but without the agents revealing their system states, dynamic models, utility functions, or uncertainties. It has to announce prices after interacting with agents via bid-price interactions. This paper examines the case where the agents respond in a compliant price-taking manner. It is shown that there is an iterative bid-price interaction, where agents respond to price announcements by complying to the requirement to announce their optimal responses according to their true stochastic model, or, in the case where the agents are linear quadratic Gaussian (LQG) systems, according to deterministic versions of their true stochastic models, that leads to the same global maximum value of social welfare attainable if all agents had pooled their information. In the important LQG case, the bid-price iteration is dramatically simple, exchanging only real-valued vectors of future prices and consumptions generations at each time step. Agents need not even know of the existence of other agents. DC power flow equations can also be incorporated. The results may be of broader interest vis-a-vis general equilibrium theory of economics for stochastic dynamic agents. Increasing renewable energy sources and innovative information and communication systems open up new challenges and opportunities to integrate distributed generation into the energy supply system. Formerly centralized energy systems need to be adapted to take full advantage of the immense potential of decentralized energy generation and smart, interconnected energy end users. We introduce a local electricity market on which prosumers and consumers of a community are able to trade electricity directly amongst each other. This local electricity market supports the local integration of renewable energy generation. It facilitates a local balance of energy supply and demand and hence reduces the need for extensive electricity transmission. We introduce, evaluate and compare two local market designs, a direct peer-to-peer market and a closed order book market, as well as two agent behaviors, zero-intelligence agents and intelligently bidding agents. We derive four scenarios by combining each market design with each agent behavior, respectively. All market scenarios offer similar economic advantages for the market participants. However, the peer-to-peer market with intelligent agents appears to be the most advantageous as it results in the lowest average overall electricity price. This paper proposes an innovative game theoretic framework for a next-generation retail electricity market (“Energy Internet”) with high penetration of distributed residential electricity suppliers (“Energy Cells”). The envisioned Energy Internet is developed for plug-and-play of a large number of distributed renewable energy generation and energy storage. The residential customers, referred as Energy Cells, are not only the electricity consumers but can also be the electricity suppliers by locally operating and managing their own distributed generators, distributed energy storage devices, and dispatchable loads. This paper formulates a set of mathematical models of the retail electricity market participants with a number of local and global constraints. A numerical case study is performed to validate the proposed next-generation retail electricity market framework using game-theoretic methodologies. The numerical simulation results demonstrate the effectiveness of the proposed market clearing scheme with high penetration of distributed residential electricity suppliers. While electric vehicles (EVs) are expected to provide environmental and economical benefit, judicious coordination of EV charging is necessary to prevent overloading of the distribution grid. Leveraging the smart grid infrastructure, the utility company can adjust the electricity price intelligently for individual customers to elicit desirable load curves. In this context, this paper addresses the problem of predicting the EV charging behavior of the consumers at different prices, which is a prerequisite for optimal price adjustment. The dependencies on price responsiveness among consumers are captured by a conditional random field (CRF) model. To account for temporal dynamics potentially in a strategic setting, the framework of online convex optimization is adopted to develop an efficient online algorithm for tracking the CRF parameters. The proposed model is then used as an input to a stochastic profit maximization module for real-time price setting. Numerical tests using simulated and semi-real data verify the effectiveness of the proposed approach. This paper presents a joint market structure for energy, spinning reserves and VAR support in a multiarea setting. It is based on a cooptimization that can simultaneously optimize all three commodities across the "seams". An auxiliary problem principle based decomposition scheme is applied to the overall optimization for coordinating interchanges of energy and ancillary services between control areas. The proposed decomposition approach preserves independent dispatching for neighboring areas while achieves overall optimum. Nodal prices for energy and opportunity cost payments to forgone energy profit due to providing reserves and VAR support are also addressed. We believe the algorithm is of particular interest in the restructuring electricity industry for resolving seams issues. An illustrative example of a modified IEEE 30-bus system is used to demonstrate the validity of proposed algorithm. This paper focuses on a scenario where a group of independently-operated investor-owned storage units seek to offer both energy and reserve in the day-ahead as well as the hour-ahead markets. We are particularly interested in the case when a significant portion of the power generated in the grid is from wind and other intermittent renewable energy sources. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units. Our design takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. We show that the formulated stochastic program can be converted into a convex optimization problem and therefore it can be solved efficiently. Our simulation results show that our design can assure profitability of the private investment on storage units. In particular, our design results in much higher profit compared to a similar but deterministic design that simply uses the expected values of the price parameters. Unlike markets for storable commodities, electricity markets depend on the real-time balance of supply and demand. Although much of the present-day grid operates effectively without storage, cost-effective ways of storing electrical energy can help make the grid more efficient and reliable. We investigate the economics of two emerging electric energy storage (EES) technologies: sodium sulfur batteries and flywheel energy storage systems in New York state's electricity market. The analysis indicates that there is a strong economic case for EES installations in the New York City region for applications such as energy arbitrage, and that significant opportunities exist throughout New York state for regulation services. Benefits from deferral of system upgrades may be important in the decision to deploy EES. Market barriers currently make it difficult for energy-limited EES such as flywheels to receive revenue for voltage regulation. Charging efficiency is more important to the economics of EES in a competitive electricity market than has generally been recognized. The problem of dynamically pricing of electricity by a retailer for customers in a demand response program is considered. It is assumed that the retailer obtains electricity in a two-settlement wholesale market consisting of a day ahead market and a real-time market. Under a day ahead dynamic pricing mechanism, the retailer aims to learn the aggregated demand function of its customers while maximizing its retail profit. A piecewise linear stochastic approximation algorithm is proposed. It is shown that the accumulative regret of the proposed algorithm grows with the learning horizon T at the order of O(log T). It is also shown that the achieved growth rate cannot be reduced by any piecewise linear policy. This paper proposes a probabilistic optimization method that produces optimal bidding curves to be submitted by an aggregator to the day-ahead electricity market and the intraday market, considering the flexible demand of his customers (based in time dependent resources such as batteries and shiftable demand) and taking into account the possible imbalance costs as well as the uncertainty of forecasts (market prices, demand, and renewable energy sources (RES) generation). The optimization strategy aims to minimize the total cost of the traded energy over a whole day, taking into account the intertemporal constraints. The proposed formulation leads to the solution of different linear optimization problems, following the natural temporal sequence of electricity spot markets. Intertemporal constraints regarding time dependent resources are fulfilled through a scheduling process performed after the day-ahead market clearing. Each of the different problems is of moderate dimension and requires short computation times. The benefits of the proposed strategy are assessed comparing the payments done by an aggregator over a sample period of one year following different deterministic and probabilistic strategies. Results show that probabilistic strategy reports better benefits for aggregators participating in power markets. This paper proposes a local electricity trading market and provides a comprehensive security analysis of this market. It first presents a market for electricity trading among individual users, and describes the different entities and the interactions among them. Based on this market model and the interactions, the paper analyses security problems and potential privacy threats imposed on users, which leads to the specification of a set of security and privacy requirements. These requirements can be used to guide the future design of secure local electricity trading markets or to perform a risk assessment of such markets. During the last years, operators of combined heat and power (CHP) plants are challenged by changing circumstances in the electricity markets. While ensuring a stable heat and power supply for customers, CHP plant operators seek to maximise the profitability of the CHP plant operation. We propose a comprehensive concept and illustrate its potential for increasing the profitability when operating a CHP plant with heat storage by participating in multiple electricity markets. For this purpose, the structure of the decision-making process consisting of bid submission, market clearing and CHP plant operation planning is represented. A multistage stochastic mixed-integer linear programming (MILP) model is developed that simultaneously optimises the operation of the CHP plant with heat storage and bidding in sequential electricity markets. Price uncertainty is captured by means of stochastic processes. The proposed concept is applied to the real case of a municipal energy supply company that participates in the German day-ahead spot and balancing market. Results of the case study illustrate how trading in sequential electricity markets can result in an increased profitability of the CHP plant operation, where additional revenues from trading offset higher generation costs. Furthermore, we exemplify how the optimal operation of the CHP plant and heat storage device are adjusted according to revenue potential in multiple electricity markets. There is growing interest in the use of grid-level storage to smooth variations in supply that are likely to arise with an increased use of wind and solar energy. Energy arbitrage, the process of buying, storing, and selling electricity to exploit variations in electricity spot prices, is becoming an important way of paying for expensive investments into grid-level storage. Independent system operators such as the New York Independent System Operator NYISO require that battery storage operators place bids into an hour-ahead market although settlements may occur in increments as small as five minutes, which is considered near "real-time". The operator has to place these bids without knowing the energy level in the battery at the beginning of the hour and simultaneously accounting for the value of leftover energy at the end of the hour. The problem is formulated as a dynamic program. We describe and employ a convergent approximate dynamic programming ADP algorithm that exploits monotonicity of the value function to find a revenue-generating bidding policy; using optimal benchmarks, we empirically show the computational benefits of the algorithm. Furthermore, we propose a distribution-free variant of the ADP algorithm that does not require any knowledge of the distribution of the price process and makes no assumptions regarding a specific real-time price model. We demonstrate that a policy trained on historical real-time price data from the NYISO using this distribution-free approach is indeed effective. The price set in electricity markets is given by the intersection of supply and demand during a given time period. The demand-side has traditionally been a price taker while the supply-side actively adjusts the output of the market clearing unit to fluctuations in consumption. Currently, there is a transition toward active demand participation that can adequately respond to market conditions. However, private knowledge of demand adjustments, such as the impact of modifying behavior or the availability to do so, creates asymmetry of information between the active supply side and the passive demand. This paper proposes a revelation mechanism that will prompt the demand-side to choose the best option for themselves among a menu of incentives. Rational behavior of consumers implies that demand will only shift when the benefit of doing so is higher than the costs of modifying consumption patterns. Given differences among demand participants and the objectives of the market operator, an analysis of the rationale of each market agent shows the feasible options for demand incentives. This study enables the design of appropriate market mechanisms aimed to discover customer categories and determine the adequate incentives for each case. In this paper, we analyze the impact of renewable energies, wind and photovoltaic, on the formation of day-ahead electricity prices at EEX. We give an overview of the policy decisions concerning the promotion of renewable energy sources in Germany and discuss their consequences on day-ahead prices. An analysis of electricity spot prices reveals that the introduction of renewable energies enhances extreme price changes. In the frame of a dynamic fundamental model, we show that there has been a continuous electricity price adaption process to market fundamentals. Furthermore, the fundamental drivers of prices differ among hours with different load profiles. Our results imply that renewable energies decrease market spot prices and have implications on the traditional fuel mix for electricity production. However, the prices for the final consumers increased overall because they must pay in addition the feed-in tariffs for the promotion of renewable energy. The increased uncertainty of the electric grid due to the penetration of renewable energy sources and deregulation of the electric market is aimed to be alleviated by demand response (DR) in the future smart grid. The demand-side resources can be incentivized to alter their consumption patterns by varying their electricity price over time. A major residential energy demand contribution is from electric heating, which, when combined with smart energy storage using water heaters, could be utilized to defer consumption to more inexpensive periods without affecting the customer's thermal quality of service. The objective is to optimize the consumer electricity price of electric storage space heating customers, in order to maximize the profit of the retailer. This approach of varying the customer electricity prices leads to a game-theoretic scenario, where the procurement and consumption profiles of the retailer and consumer agents are based on the set electricity price. The optimization of the consumer electricity price is shown to offer lesser expense for the retailer. In addition, hourly load-following can be improved by offering further discounts for the consumers. This paper determines the optimal bidding strategy of an electric vehicle (EV) aggregator participating in day-ahead energy and regulation markets using stochastic optimization. Key sources of uncertainty affecting the bidding strategy are identified and incorporated in the stochastic optimization model. The aggregator portfolio optimization model should include inevitable deviations between day-ahead cleared bids and actual real-time energy purchases as well as uncertainty for the energy content of regulation signals in order to ensure profit maximization and reliable reserve provision. Energy deviations are characterized as “uninstructed” or “instructed” depending on whether or not the responsibility resides with the aggregator. Price deviations and statistical characteristics of regulation signals are also investigated. Finally, a new battery model is proposed for better approximation of the battery charging characteristic. Test results with an EV aggregator representing one thousand EVs are presented and discussed. An optimal supply and demand bidding, scheduling, and deployment design framework is proposed for battery systems. It takes into account various design factors such as the day-ahead and real-time market prices and their statistical dependency, as well as the location, size, efficiency, lifetime, and charge and discharge rates of the batteries. Utilizing second-life used batteries is also considered. Without loss of generality, our focus is on the California Independent System Operator (ISO) energy market and its two available bidding options, namely self-schedule bidding and economic bidding. While the formulated stochastic optimization problems are originally nonlinear and difficult to solve, we propose a methodology to decompose them into inner and outer subproblems. Accordingly, we find the global optimal solutions within a short amount of computational time. All case studies in this paper are based on real market data.