Energy Storage Sharing Strategy in Distribution Networks Using Bi-level Optimization Approach

In this paper, we address the energy storage management problem in distribution networks from the perspective of an independent energy storage manager (IESM) who aims to realize optimal energy storage sharing with multi-objective optimization, i.e., optimizing the system peak loads and the electricity purchase costs of the distribution company (DisCo) and its customers. To achieve the goal of the IESM, an energy storage sharing strategy is therefore proposed, which allows DisCo and customers to control the assigned energy storage. The strategy is updated day by day according to the system information change. The problem is formulated as a bi-level mathematical model where the upper level model (ULM) seeks for optimal division of energy storage among Disco and customers, and the lower level models (LLMs) represent the minimizations of the electricity purchase costs of DisCo and customers. Further, in order to enhance the computation efficiency, we transform the bi-level model into a single-level mathematical program with equilibrium constraints (MPEC) model and linearize it. Finally, we validate the effectiveness of the strategy and complement our analysis through case studies

Variable Weighted Multi-Objective Multi-Dimensional Genetic Algorithm for Demand Response Scheduling in a Smart Grid

This research presents the optimized scheduling of demand response loads of a residential community of 30 houses using a multi-objective multi-dimensional genetic algorithm (MOMD-GA) with a variable weighted objective function. Incorporating day ahead hourly real time pricing (RTP), the MOMD-GA attempts to present possible optimized dispatch patterns with their associated penalties and constraints (environmental, consumers and suppliers) thus providing system operators (SOs) and distribution network operators (DNOs) sufficient data for real time decision making. The variable weights for each considered component of the cost function is chosen to force the MOMD-GA towards exploring optimum solutions with lower environmental cost. Further shown are the trade-offs in selecting particular dispatch bias (consumer, supplier, environmental and optimized) and the impact of the various dispatch scenarios on the cost of overall electricity bill of the community

Cost and emission savings from the deployment of variable electricity tariffs and advanced domestic energy hub storage management

This paper uses the energy hub concept to holistically model future energy infrastructure in domestic buildings, including energy storage. The developed model allows the deployment of a novel bi-criteria optimization algorithm for minimizing both the cost and emissions of energy hub operation whilst taking advantage of dynamic tariffs. Unlike the traditional flat rate tariffs, the dynamic tariffs employed in this paper reflect variations in the wholesale energy market, and are used as commercial inputs to drive the storage operation and reduce both costs and emissions. The developed algorithm and hub model are used to optimize an example energy hub against four 24 hour periods of loads and dynamic tariffs, one from each season. Annual savings are estimated and compared against a base case, with no storage or management, of a typical house in the UK, showing significant cost and emissions savings

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

A novel controller of a battery-supercapacitor hybrid energy storage system for domestic applications

Electrical energy storage is an attractive technology for complementing domestic scale Combined Heat and Power (CHP) because when CHP is dispatched to meet the heating load, the storage can reconcile any mismatch between the electrical load and CHP generation. Hybridization of electrical storage technologies reduces the compromise between power and energy density and extends storage system lifetime but necessitates a more complex control scheme. This paper proposes a novel control scheme for a domestic battery-supercapacitor hybrid energy storage system (HESS) for use with micro-combined heat and power (micro-CHP) generation. The proposed HESS controller utilizes the low frequency component of the supercapacitor voltage to generate the battery reference current, which not only allocates low frequency power to the battery but also simultaneously maintain the battery current and the supercapacitor voltage within their predefined limits. The negative effects of the 100 Hz ripple component in the supercapacitor current, such as overheating and increased converter losses have been hitherto neglected in the literature and are addressed here for the first time by adding a 100 Hz band-stop filter in the supercapacitor controller. Simulink simulations and signal hardware-in-the-loop (SHIL) real-time simulations have been conducted to demonstrate the effective operation of the HESS