Microgrids represent an emerging paradigm of future electric power systems that can utilize both distributed and centralized generations. Two recent trends in microgrids are the integration of local renewable energy sources (such as wind farms) and the use of co-generation (i.e., to supply both electricity and heat). However, these trends also bring unprecedented challenges to the design of intelligent control strategies for microgrids. Traditional generation scheduling paradigms rely on perfect prediction of future electricity supply and demand. They are no longer applicable to microgrids with unpredictable renewable energy supply and with co-generation (that needs to consider both electricity and heat demand). In this paper, we study online algorithms for the microgrid generation scheduling problem with intermittent renewable energy sources and co-generation, with the goal of maximizing the cost-savings with local generation. Based on the insights from the structure of the offline optimal solution, we propose a class of competitive online algorithms, called CHASE (Competitive Heuristic Algorithm for Scheduling Energy-generation), that track the offline optimal in an online fashion. Under typical settings, we show that CHASE achieves the best competitive ratio among all deterministic online algorithms, and the ratio is no larger than a small constant 3.
Several recent works have started to study energy generation strategies for microgrids. For example, the authors in @cite_33 develop a linear programming based cost minimization approach for UC in microgrids. @cite_24 considers the fuel consumption rate minimization in microgrids and advocates to build ICT infrastructure in microgrids. @cite_18 @cite_27 discuss the energy scheduling problems in data centers, whose models are similar with ours. The difference between these works and ours is that they assume the demand and energy supply are given beforehand, and ours does not rely on input prediction.
@cite_24 @cite_27 @cite_18 @cite_33
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Microgrid (MG) is a promising component for future smart grid (SG) deployment. The balance of supply and demand of electric energy is one of the most important requirements of MG management. In this paper, we present a novel framework for smart energy management based on the concept of quality-of-service in electricity (QoSE). Specifically, the resident electricity demand is classified into basic usage and quality usage. The basic usage is always guaranteed by the MG, while the quality usage is controlled based on the MG state. The microgrid control center (MGCC) aims to minimize the MG operation cost and maintain the outage probability of quality usage, i.e., QoSE, below a target value, by scheduling electricity among renewable energy resources, energy storage systems, and macrogrid. The problem is formulated as a constrained stochastic programming problem. The Lyapunov optimization technique is then applied to derive an adaptive electricity scheduling algorithm by introducing the QoSE virtual queues and energy storage virtual queues. The proposed algorithm is an online algorithm since it does not require any statistics and future knowledge of the electricity supply, demand and price processes. We derive several "hard" performance bounds for the proposed algorithm, and evaluate its performance with trace-driven simulations. The simulation results demonstrate the efficacy of the proposed electricity scheduling algorithm. A cost optimization scheme for a microgrid is presented. Prior to the optimization of the microgrid itself, several schemes for sharing power between two generators are compared. The minimization of fuel use in a microgrid with a variety of power sources is then discussed. The optimization of a small power system has important differences from the case of a large system and its traditional economic dispatch problem. Among the most important differences is the presence of a local heat demand which adds another dimension to the optimization problem. The microgrid considered in this paper consists of two reciprocating gas engines, a combined heat and power plant, a photovoltaic array and a wind generator. The optimization is aimed at reducing the fuel consumption rate of the system while constraining it to fulfil the local energy demand (both electrical and thermal) and provide a certain minimum reserve power. A penalty is applied for any heat produced in excess of demand. The solution of the optimization problem strongly supports the idea of having a communication infrastructure operating between the power sources. This paper presents a model for microgrid optimal scheduling considering multi-period islanding constraints. The objective of the problem is to minimize the microgrid total operation cost which comprises the generation cost of local resources and cost of energy purchase from the main grid. The microgrid optimal scheduling problem is decomposed into a grid-connected operation master problem and an islanded operation subproblem. The microgrid capability in operating in the islanded mode for multiple hours is scrutinized by a T-τ islanding criterion. The integer scheduling decisions determined in the master problem will be examined against the microgrid islanding feasibility in the subproblem. The scheduling decisions will be revised using proper islanding cuts if sufficient generation is not available to guarantee a feasible islanding. Islanding cuts will revise generating units, energy storage systems, and adjustable loads schedules. Any change in the schedule of adjustable loads outside the operating time interval specified by consumers is penalized by an inconvenience factor in the objective. Numerical simulations demonstrate the effectiveness of the proposed microgrid optimal scheduling model and explore its economic and reliability merits. This article develops a linear programming cost minimisation model for the high level system design and corresponding unit commitment of generators and storage within a microgrid; a set of energy resources working co-operatively to create a cost effective, reliable and environmentally friendly energy provision system. Previous work in this area is used as a basis for formulation of a new approach to this problem, with particular emphasis on why a microgrid is different to centralised generation or other grid-connected decentralised energy resources. Specifically, the model explicitly defines the amount of time that the microgrid would be expected to operate autonomously, and restricts flow of heat between microgrid participants to defined cases. The model developed is applied to a set of United Kingdom commercial load profiles, under best current estimates of energy prices and technology capital costs, to determine investment attractiveness of the microgrid. Sensitivity analysis of results to variations in energy prices is performed. The results broadly indicate that a microgrid can offer an economic proposition, although it is necessarily slightly more expensive than regular grid-connected decentralised generation. The analysis results have raised important questions regarding a fair method for settlement between microgrid participants, and game theory has been identified as a suitable tool to analyse aspects of this situation.