Abstract
The implementation of real-time price-based demand response program and integration of renewable energy resources (RESs) improves efficiency and ensure stability of electric grid. This paper proposes a novel intelligent optimization based demand-side management (DSM) framework for smart grid integrated with RESs. In the intelligent DSM framework the artificial neural network (ANN) forecasts energy usage behavior of consumers and real-time price-based demand response program (RTPDRP) of electric utility company (EUC). The smart energy management controller of the proposed intelligent DSM framework adapts forecasted energy usage behavior of consumers using forecasted RTPDRP to create operation schedule. The consumers implement the created schedule to minimize energy cost, peak load, carbon emission subjected to improving user comfort and avoiding rebound peaks. Simulations are conducted using our proposed hybrid genetic ant colony (HGAC) optimization algorithm to create schedule for three cases: EUC without RESs, EUC with RESs, and EUC with both RESs and storage technologies. To endorse the applicability and productivity of the proposed DSM framework based on HGAC optimization algorithm with five existing algorithms based frameworks. Simulation results show that the proposed DSM framework is superior compared with the existing frameworks in terms of energy cost minimization, peak load mitigation, carbon emission alleviation, and user discomfort minimization. The proposed HGAC optimization algorithm reduced electricity cost, carbon emission, and peak load by 12.16%, 4.00%, and 19.44% in case I; by 26.8%, 20.71%, and 33.3% in case II; and by 24.4%, 16.44%, and 37.08% in case III, respectively, compared to without scheduling.
Highlights
T HROUGHOUT, the world energy demand is rapidly increasing with the drastic increase in population and modern technology
A system model is proposed based on artificial neural network (ANN) and novel algorithm namely hybrid genetic ant colony (HGAC) optimization algorithm for solving problems accompanied with intrinsic models while catering demand-side management (DSM) problem
We scheduled the user load by using the heuristic algorithms that are proposed in our scheme, and compared the results with unscheduled load in terms of electricity cost, peak to average ratio (PAR) and carbon emission
Summary
T HROUGHOUT, the world energy demand is rapidly increasing with the drastic increase in population and modern technology. Authors considered a case of residential consumers with integrated ESS and PV to solve energy management problem in [5] Their proposed algorithm minimized electricity cost and PAR by scheduling the operation time of different appliances. State-of-the-art work is concluded with following findings: (i) there is no model exist which is perfect in all aspects, (ii) there are some parameters, which are conflicting in nature due which tradeoff exist, increasing one will results a decrease in the other and vice versa In this context, a system model is proposed based on ANN and novel algorithm namely hybrid genetic ant colony (HGAC) optimization algorithm for solving problems accompanied with intrinsic models while catering DSM problem
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