Abstract
This paper presents a demand response (DR) framework that intertwines thermodynamic building models with a genetic algorithm (GA)-based optimization method. The framework optimizes heating/cooling schedules of end-users inside a business park microgrid with local distributed generation from renewable energy sources (DG-RES) based on two separate objectives: net load minimization and electricity cost minimization. DG-RES is treated as a curtailable resource in anticipation of future scenarios where the infeed of DG-RES to the regional distribution network could be limited. We test the DR framework with a case study of a refrigerated warehouse and an office building located in a business park with local PV generation. Results show the technical potential of the DR framework in harnessing the flexibility of the thermal masses from end-user sites in order to: (1) reduce the energy exchange at the point of connection; (2) reduce the cost of electricity for the microgrid end-users; and (3) increase the local utilization of DG-RES in cases where DG-RES exports to the grid are restricted. The results of this work can aid end-users and distribution network operators to reduce energy costs and energy consumption.
Highlights
The stochastic nature of solar and wind energy resources poses several challenges to the large-scale integration of distributed generation from renewable energy sources (DG-RES) into electricity networks, mainly in terms of reliability and economical feasibility [1,2,3]
The demand response (DR) framework we propose enables: 1. The operation of commercial and industrial (C&I) areas as grid-connected smart microgrids in order to support the local use of DG-RES through flexible demand, optimize multiple building schedules at the same time through a common goal and create benefits for the stakeholders involved
We present the effect of DR in our smart microgrid, with regard to the energy consumption and the cost minimization problems solved by our proposed genetic algorithm (GA)-based controller
Summary
The stochastic nature of solar and wind energy resources poses several challenges to the large-scale integration of distributed generation from renewable energy sources (DG-RES) into electricity networks, mainly in terms of reliability and economical feasibility [1,2,3]. The concept of smart grids encompasses different technical solutions that enable flexibility from other sources, such that consumption and/or generation can be shifted with respect to time. This can be achieved through enhanced monitoring and control functionalities, the use of (electrical and/or thermal) buffers and increased consumer participation through demand response (DR) programs [5,6].
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