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 They are no longer applicable to microgrids with unpredictable renewable energy supply and with co-generation (that needs to consider both electricity and heat demand).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.
Online optimization and algorithm design is an established approach in optimizing the performance of various computer systems with minimum knowledge of inputs @cite_34 @cite_25 . Recently, it has found new applications in data centers @cite_39 @cite_22 @cite_1 @cite_7 @cite_11 @cite_5 . To the best of our knowledge, our work is the first to study the competitive online algorithms for energy generation in microgrids with intermittent energy sources and co-generation. The authors in @cite_26 apply online convex optimization framework @cite_17 to design ED algorithms for microgrids. The authors in @cite_0 adopt Lyapunov optimization framework @cite_25 to design electricity scheduling for microgrids, with consideration of energy storage. However, neither of the above considers the startup cost of the local generations. In contrast, our work jointly consider UC and ED in microgrids with co-generation. Furthermore, the above three works adopt different frameworks and provide online algorithms with different types of performance guarantee.
@cite_26 @cite_22 @cite_7 @cite_1 @cite_17 @cite_39 @cite_0 @cite_5 @cite_34 @cite_25 @cite_11
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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. Energy management in microgrids is typically formulated as an offline optimization problem for day-ahead scheduling by previous studies. Most of these offline approaches assume perfect forecasting of the renewables, the demands, and the market, which is difficult to achieve in practice. Existing online algorithms, on the other hand, oversimplify the microgrid model by only considering the aggregate supply-demand balance while omitting the underlying power distribution network and the associated power flow and system operational constraints. Consequently, such approaches may result in control decisions that violate the real-world constraints. This paper focuses on developing an online energy management strategy (EMS) for real-time operation of microgrids that takes into account the power flow and system operational constraints on a distribution network. We model the online energy management as a stochastic optimal power flow problem and propose an online EMS based on Lyapunov optimization. The proposed online EMS is subsequently applied to a real-microgrid system. The simulation results demonstrate that the performance of the proposed EMS exceeds a greedy algorithm and is close to an optimal offline algorithm. Lastly, the effect of the underlying network structure on energy management is observed and analyzed. The design of online algorithms for maximizing the achievable rate in a wireless communication channel between a source and a destination over a fixed number of slots is considered. The source is assumed to be powered by a natural renewable source, and the most general case of arbitrarily varying energy arrivals is considered, where neither the future energy arrival instants or amount nor their distribution is known. The fading coefficients are also assumed to be arbitrarily varying over time, with only causal information available at the source. For a maximization problem, the utility of an online algorithm is tested by finding its competitive ratio or competitiveness that is defined to be the maximum of the ratio of the gain of the optimal offline algorithm and the gain of the online algorithm over all input sequences. We show that the lower bound on the optimal competitive ratio for maximizing the achievable rate is arbitrarily close to the number of slots. Conversely, we propose a simple strategy that invests available energy uniformly over all remaining slots until the next energy arrival, and show that its competitive ratio is equal to the number of slots, to conclude that it is an optimal online 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.