This paper, by comparing three potential energy trading systems, studies the feasibility of integrating a community energy storage (CES) device with consumer-owned photovoltaic (PV) systems for demand-side management of a residential neighborhood area network. We consider a fully-competitive CES operator in a non-cooperative Stackelberg game, a benevolent CES operator that has socially favorable regulations with competitive users, and a centralized cooperative CES operator that minimizes the total community energy cost. The former two game-theoretic systems consider that the CES operator first maximizes their revenue by setting a price signal and trading energy with the grid. Then the users with PV panels play a non-cooperative repeated game following the actions of the CES operator to trade energy with the CES device and the grid to minimize energy costs. The centralized CES operator cooperates with the users to minimize the total community energy cost without appropriate incentives. The non-cooperative Stackelberg game with the fully-competitive CES operator has a unique Stackelberg equilibrium at which the CES operator maximizes revenue and users obtain unique Pareto-optimal Nash equilibrium CES energy trading strategies. Extensive simulations show that the fully-competitive CES model gives the best trade-off of operating environment between the CES operator and the users.
Prior works have examined centralized control of distributed power resources, such as renewable power sources and storage devices, for effective energy management @cite_25 @cite_33 . Decentralized control of energy resources has been proposed to increase system reliability and robustness @cite_24 . In particular, game theory has been applied to analyze interactions between distributed energy resources in power system @cite_0 @cite_12 . The authors in @cite_23 achieve cost-effective energy management through a non-cooperative game that schedules consumer-owned energy storage devices and appliances. In @cite_7 , the authors study a cooperative game-theoretic approach to achieve optimal load balancing using a CES device where users share the stored energy of the CES device to contribute towards community's overall demand-side management. In contrast, we investigate a non-cooperative hierarchical energy trading system between a CES device and users based on Stackelberg game theory. Moreover, compared to @cite_7 , the charging and discharging mechanism of the CES device in this paper employs user-owned PV energy generation to achieve demand-side management.
@cite_33 @cite_7 @cite_24 @cite_0 @cite_23 @cite_25 @cite_12
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This paper presents a new method based on the cost-benefit analysis for optimal sizing of an energy storage system in a microgrid (MG). The unit commitment problem with spinning reserve for MG is considered in this method. Time series and feed-forward neural network techniques are used for forecasting the wind speed and solar radiations respectively and the forecasting errors are also considered in this paper. Two mathematical models have been built for both the islanded and grid-connected modes of MGs. The main problem is formulated as a mixed linear integer problem (MLIP), which is solved in AMPL (A Modeling Language for Mathematical Programming). The effectiveness of the approach is validated by case studies where the optimal system energy storage ratings for the islanded and grid-connected MGs are determined. Quantitative results show that the optimal size of BESS exists and differs for both the grid-connected and islanded MGs in this paper. In this paper, we propose a model for households to share energy from community energy storage (CES) such that both households and utility company benefit from CES. In addition to providing a range of ancillary grid services, CES can also be used for demand side management, to shave peaks and fill valleys in system load. We introduce a method stemming from consumer theory and cooperative game theory that uses CES to balance the load of an entire locality and manage household energy allocations respectively. Load balancing is derived as a geometric programming problem. Each households contribution to overall non-uniformity of the load profile is modeled using a characteristic function and Shapley values are used to allocate the amount and price of surplus energy stored in CES. The proposed method is able to perfectly balance the load while also making sure that each household is guaranteed a reduction in energy costs. A power system is a collection of individual components that compete for system resources. This paper presents a game theoretic approach to the control decision process of individual sources and loads in small-scale and dc power systems. Framing the power system as a game between players facilitates the definition of individual objectives, which adds modularity and adaptability. The proposed methodology enhances the reliability and robustness of the system by avoiding the need for a central or supervisory control. It is also a way to integrate and combine supply and demand side management into a single approach. Examples are presented that use a simple nine bus dc power system to demonstrate the proposed method for various scenarios and player formulations. The future smart grid is envisioned as a large scale cyberphysical system encompassing advanced power, communications, control, and computing technologies. To accommodate these technologies, it will have to build on solid mathematical tools that can ensure an efficient and robust operation of such heterogeneous and large-scale cyberphysical systems. In this context, this article is an overview on the potential of applying game theory for addressing relevant and timely open problems in three emerging areas that pertain to the smart grid: microgrid systems, demand-side management, and communications. In each area, the state-of-the-art contributions are gathered and a systematic treatment, using game theory, of some of the most relevant problems for future power systems is provided. Future opportunities for adopting game-theoretic methodologies in the transition from legacy systems toward smart and intelligent grids are also discussed. In a nutshell, this article provides a comprehensive account of the application of game theory in smart grid systems tailored to the interdisciplinary characteristics of these systems that integrate components from power systems, networking, communications, and control. For the efficient operation of the smart grid, it is important that there is an instant by instant matching between the electricity supply and the power consumption. Electrical power storage provides a viable solution to managing power supply and electrical loads as well as unexpected imbalances. Electricity suppliers could deploy electricity storage facilities at various levels of the smart grid system: generation, transmission, substations and residential level. Storage would significantly address the power quality and reliability problems through peak shaving and frequency control. It also reduces the need for huge infrastructural expenditures by making them more efficient. At the residential level, smart storage together with dynamic pricing in the deregulated electricity markets presents the electricity suppliers with a strategy to achieve grid stability. In this paper, we consider a smart grid environment with a high penetration of households' storage batteries. By using an appropriate electricity price structure, the electricity supplier influences households' electricity consumption. On the other hand, the households aim to minimize their electricity bills by capitalizing on price fluctuation to schedule their electrical appliances and coordinate the charging and discharging of their batteries. The electricity supplier has a dynamic power limit for each hour that must not be exceeded by the hourly aggregate load of the households. Further, we assume that in supplying electrical power, the households' electrical devices are given priority over their storage devices. The policy is such that batteries will be charged by the residual power after the appliances loads have been satisfied. The households have to compete for the residual electricity so as to maximize the state of charge of their batteries. We have therefore modeled this system as a non-cooperative Nash equilibrium game where the households are considered as selfish but rational players whose objectives are to optimize their individual utilities. The development of energy management tools for next-generation PhotoVoltaic (PV) installations, including storage units, provides flexibility to distribution system operators. In this paper, the aggregation and implementation of these determinist energy management methods for business customers in a microgrid power system are presented. This paper proposes a determinist energy management system for a microgrid, including advanced PV generators with embedded storage units and a gas microturbine. The system is organized according to different functions and is implemented in two parts: a central energy management of the microgrid and a local power management at the customer side. The power planning is designed according to the prediction for PV power production and the load forecasting. The central and local management systems exchange data and order through a communication network. According to received grid power references, additional functions are also designed to manage locally the power flows between the various sources. Application to the case of a hybrid supercapacitor battery-based PV active generator is presented.