Abstract
This paper presents a novel control algorithm for optimising operational costs of a combined domestic micro-CHP (combined heat and power), battery and heat storage system. Using a minute by minute basic time-step, this work proposes a simple and computationally efficient rule based whole-system management, developed from empirical study of realistic simulated domestic electricity and heat loads. The CHP availability is considered in two binary states which, together with leveraging storage effectively, maximises CHP efficiency, and gives the algorithm increased real world feasibility. In addition, a novel application of a dual battery system is proposed to support the micro-CHP with each battery supplying just one of the distinctive morning and evening electrical load peaks, and thus inherently improving overall battery system lifetime. A case study is presented where the algorithm is shown to yield approximately 23% energy cost savings above the base case, almost 3% higher savings than that of the closest previous work, and 96.8% of the theoretical minimum cost. In general, the algorithm is shown to always yield better than 88% of the theoretical minimum cost, a ratio that will be considerably higher when real-world CHP limitations are factored into the theoretical minimum calculation.
Highlights
Large industrial and commercial customers have begun to participate in Smart Grid (SG) programs, for example, demand side management (DSM) and demand response (DR), to save energy costs and reduce CO2 emissions
As reported by the U.S Department of Energy, buildings consume more energy compared with other broad sectors of energy consumption, such as industry and transportation, approximately 40% compared to 30% each respectively [3]
The energy tariffs and demands which are shown in Figures 6 and 7, yield a daily gas cost of 293 pence and a daily electricity cost of 308.45 pence
Summary
Large industrial and commercial customers have begun to participate in Smart Grid (SG) programs, for example, demand side management (DSM) and demand response (DR), to save energy costs and reduce CO2 emissions. The domestic sector has shown less interest in SG technologies, because of their individually smaller impact on the grid and the technical difficulties in aggregating large numbers of customers [1,2]. As reported by the U.S Department of Energy, buildings consume more energy compared with other broad sectors of energy consumption, such as industry and transportation, approximately 40% compared to 30% each respectively [3]. It is urgent to develop cost-effective and practical methods to control energy consumption in residential dwellings. In order to meet emission reduction targets, renewable energy has been deployed in an increasingly localised and decentralised manner, in the form of distributed generation (DG) [5]
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