Distributed allocation of a shared energy storage system in a microgrid

The economic management of a microgrid can greatly benefit from energy storage systems (ESSs), which may act as virtual load deferral systems to take advantage of the fluctuations of energy prices and accommodate for demand-production mismatches caused by the scarce predictability of renewable sources. In a distributed energy management scenario, an ESS may serve multiple users, a setting which calls for the development of suitable resource allocation policies for the storage capacity. In particular, distributed control policies are of interest, where each user operates independently with the least exchange of information with the other users. A methodology is developed in the paper for such purpose, based on an iterative resource allocation mechanism, realized by means of a negotiation process among users, resembling stock exchange dynamics. The resulting distributed strategy for the management of the shared resource comes close to optimality at a low computational cost, which is affordable in large scale practical applications. It is also robust to communication failures between users

Credit-based distributed real-time energy storage sharing management

Abstract: In this paper, energy storage sharing among a group of cooperative households with integrated renewable generations in a grid-connected microgrid is studied. In such a microgrid, a group of households, who are willing to cooperatively operate a shared energy storage via a central coordinator, aims to minimize their long term time-averaged costs, by jointly taking into account the operational constraints of the shared energy storage, the stochastic solar power generations and the time-varying load demands from all households, as well as the fluctuating electricity prices. This energy management problem, which comprises storage management and load control, is first formulated as a constrained stochastic programming problem. Based on the Lyapunov optimization theory, a distributed real-time sharing control algorithm is proposed to solve the constrained stochastic programming problem without requiring any statistical knowledge of the stochastic renewable energy generations and the uncertain power loads. The credit-based distributed sharing algorithm, in which each household independently solves a simple convex optimization problem without requiring any statistics of the system, is designed to quickly adapt to the system dynamics while facilitating a fair allocation of the shared energy storage with respect to individual households’ energy contributions. The performance gap of the proposed low-complexity distributed sharing algorithm is evaluated via theoretical analysis. Numerical simulations using a practical system setup are conducted to investigate the effectiveness of the proposed sharing control algorithm in terms of energy cost saving and fairness. The simulation results show that the proposed credit-based distributed sharing algorithm can not only save power consumption cost by coordinating the use the shared battery among households in a fair manner but also improve the utilization of renewable energy generation

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

Smart microgrids and virtual power plants in a hierarchical control structure

In order to achieve a coordinated integration of distributed energy resources in the electrical network, an aggregation of these resources is required. Microgrids and virtual power plants (VPPs) address this issue. Opposed to VPPs, microgrids have the functionality of islanding, for which specific control strategies have been developed. These control strategies are classified under the primary control strategies. Microgrid secondary control deals with other aspects such as resource allocation, economic optimization and voltage profile improvements. When focussing on the control-aspects of DER, VPP coordination is similar with the microgrid secondary control strategy, and thus, operates at a slower time frame as compared to the primary control and can take full advantage of the available communication provided by the overlaying smart grid. Therefore, the feasibility of the microgrid secondary control for application in VPPs is discussed in this paper. A hierarchical control structure is presented in which, firstly, smart microgrids deal with local issues in a primary and secondary control. Secondly, these microgrids are aggregated in a VPP that enables the tertiary control, forming the link with the electricity markets and dealing with issues on a larger scale

A Community Microgrid Architecture with an Internal Local Market

This work fits in the context of community microgrids, where members of a community can exchange energy and services among themselves, without going through the usual channels of the public electricity grid. We introduce and analyze a framework to operate a community microgrid, and to share the resulting revenues and costs among its members. A market-oriented pricing of energy exchanges within the community is obtained by implementing an internal local market based on the marginal pricing scheme. The market aims at maximizing the social welfare of the community, thanks to the more efficient allocation of resources, the reduction of the peak power to be paid, and the increased amount of reserve, achieved at an aggregate level. A community microgrid operator, acting as a benevolent planner, redistributes revenues and costs among the members, in such a way that the solution achieved by each member within the community is not worse than the solution it would achieve by acting individually. In this way, each member is incentivized to participate in the community on a voluntary basis. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator. Numerical results obtained on a real test case implemented in Belgium show around 54% cost savings on a yearly scale for the community, as compared to the case when its members act individually.Comment: 16 pages, 15 figure

Smart Microgrids: Overview and Outlook

The idea of changing our energy system from a hierarchical design into a set of nearly independent microgrids becomes feasible with the availability of small renewable energy generators. The smart microgrid concept comes with several challenges in research and engineering targeting load balancing, pricing, consumer integration and home automation. In this paper we first provide an overview on these challenges and present approaches that target the problems identified. While there exist promising algorithms for the particular field, we see a missing integration which specifically targets smart microgrids. Therefore, we propose an architecture that integrates the presented approaches and defines interfaces between the identified components such as generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid Worksho