Management of Renewable Energy for a Shared Facility Controller in Smart Grid

This paper proposes an energy management scheme to maximize the use of solar energy in the smart grid. In this context, a shared facility controller (SFC) with a number of solar photovoltaic panels in a smart community is considered that has the capability to schedule the generated energy for consumption and trade to other entities. In particular, a mechanism is designed for the SFC to decide on the energy surplus, if there is any, that it can use to charge its battery and sell to the households and the grid based on the offered prices. In this regard, a hierarchical energy management scheme is proposed with a view to reduce the total operational cost to the SFC. The concept of a virtual cost is introduced that aids the SFC to estimate its future operational cost based on some available current information. The energy management is conducted for three different cases, and the optimal cost to the SFC is determined for each case by the theory of maxima and minima. A real-time algorithm is proposed to reach the optimal cost for all cases, and some numerical examples are provided to demonstrate the beneficial properties of the proposed schem

Management of Renewable Energy for a Shared Facility Controller in Smart Grid

© 2016 IEEE. This paper proposes an energy management scheme to maximize the use of solar energy in the smart grid. In this context, a shared facility controller (SFC) with a number of solar photovoltaic panels in a smart community is considered that has the capability to schedule the generated energy for consumption and trade to other entities. In particular, a mechanism is designed for the SFC to decide on the energy surplus, if there is any, that it can use to charge its battery and sell to the households and the grid based on the offered prices. In this regard, a hierarchical energy management scheme is proposed with a view to reduce the total operational cost to the SFC. The concept of a virtual cost is introduced that aids the SFC to estimate its future operational cost based on some available current information. The energy management is conducted for three different cases, and the optimal cost to the SFC is determined for each case by the theory of maxima and minima. A real-time algorithm is proposed to reach the optimal cost for all cases, and some numerical examples are provided to demonstrate the beneficial properties of the proposed scheme

Energy Management for a User Interactive Smart Community: A Stackelberg Game Approach

This paper studies a three party energy management problem in a user interactive smart community that consists of a large number of residential units (RUs) with distributed energy resources (DERs), a shared facility controller (SFC) and the main grid. A Stackelberg game is formulated to benefit both the SFC and RUs, in terms of incurred cost and achieved utility respectively, from their energy trading with each other and the grid. The properties of the game are studied and it is shown that there exists a unique Stackelberg equilibrium (SE). A novel algorithm is proposed that can be implemented in a distributed fashion by both RUs and the SFC to reach the SE. The convergence of the algorithm is also proven, and shown to always reach the SE. Numerical examples are used to assess the properties and effectiveness of the proposed scheme.Comment: 6 pages, 4 figure

Two-Stage Consensus-Based Distributed MPC for Interconnected Microgrids

In this paper, we propose a model predictive control based two-stage energy management system that aims at increasing the renewable infeed in interconnected microgrids (MGs). In particular, the proposed approach ensures that each MG in the network benefits from power exchange. In the first stage, the optimal islanded operational cost of each MG is obtained. In the second stage, the power exchange is determined such that the operational cost of each MG is below the optimal islanded cost from the first stage. In this stage, a distributed augmented Lagrangian method is used to solve the optimisation problem and determine the power flow of the network without requiring a central entity. This algorithm has faster convergence and same information exchange at each iteration as the dual decomposition algorithm. The properties of the algorithm are illustrated in a numerical case study

European White Book on Real-Time Power Hardware in the Loop Testing : DERlab Report No. R- 005.0

The European White Book on Real-Time-Powerhardware-in-the-Loop testing is intended to serve as a reference document on the future of testing of electrical power equipment, with specifi c focus on the emerging hardware-in-the-loop activities and application thereof within testing facilities and procedures. It will provide an outlook of how this powerful tool can be utilised to support the development, testing and validation of specifi cally DER equipment. It aims to report on international experience gained thus far and provides case studies on developments and specifi c technical issues, such as the hardware/software interface. This white book compliments the already existing series of DERlab European white books, covering topics such as grid-inverters and grid-connected storag

A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries

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

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid

This paper investigates the feasibility of using a discriminate pricing scheme to offset the inconvenience that is experienced by an energy user (EU) in trading its energy with an energy controller in smart grid. The main objective is to encourage EUs with small distributed energy resources (DERs), or with high sensitivity to their inconvenience, to take part in the energy trading via providing incentive to them with relatively higher payment at the same time as reducing the total cost to the energy controller. The proposed scheme is modeled through a two-stage Stackelberg game that describes the energy trading between a shared facility authority (SFA) and EUs in a smart community. A suitable cost function is proposed for the SFA to leverage the generation of discriminate pricing according to the inconvenience experienced by each EU. It is shown that the game has a unique sub-game perfect equilibrium (SPE), under the certain condition at which the SFA's total cost is minimized, and that each EU receives its best utility according to its associated inconvenience for the given price. A backward induction technique is used to derive a closed form expression for the price function at SPE, and thus the dependency of price on an EU's different decision parameters is explained for the studied system. Numerical examples are provided to show the beneficial properties of the proposed scheme.Comment: 7 pages, 4 figures, 3 tables, conference pape

Upscaling energy control from building to districts: current limitations and future perspectives

Due to the complexity and increasing decentralisation of the energy infrastructure, as well as growing penetration of renewable generation and proliferation of energy prosumers, the way in which energy consumption in buildings is managed must change. Buildings need to be considered as active participants in a complex and wider district-level energy landscape. To achieve this, the authors argue the need for a new generation of energy control systems capable of adapting to near real-time environmental conditions while maximising the use of renewables and minimising energy demand within a district environment. This will be enabled by cloud-based demand-response strategies through advanced data analytics and optimisation, underpinned by semantic data models as demonstrated by the Computational Urban Sustainability Platform, CUSP, prototype presented in this paper. The growing popularity of time of use tariffs and smart, IoT connected devices offer opportunities for Energy Service Companies, ESCo’s, to play a significant role in this new energy landscape. They could provide energy management and cost savings for adaptable users, while meeting energy and CO2 reduction targets. The paper provides a critical review and agenda setting perspective for energy management in buildings and beyond

Optimal Scheduling of Energy Storage Using A New Priority-Based Smart Grid Control Method

This paper presents a method to optimally use an energy storage system (such as a battery) on a microgrid with load and photovoltaic generation. The purpose of the method is to employ the photovoltaic generation and energy storage systems to reduce the main grid bill, which includes an energy cost and a power peak cost. The method predicts the loads and generation power of each day, and then searches for an optimal storage behavior plan for the energy storage system according to these predictions. However, this plan is not followed in an open-loop control structure as in previous publications, but provided to a real-time decision algorithm, which also considers real power measures. This algorithm considers a series of device priorities in addition to the storage plan, which makes it robust enough to comply with unpredicted situations. The whole proposed method is implemented on a real-hardware test bench, with its different steps being distributed between a personal computer and a programmable logic controller according to their time scale. When compared to a different state-of-the-art method, the proposed method is concluded to better adjust the energy storage system usage to the photovoltaic generation and general consumption.Unión Europea ID 100205Unión Europea ID 26937