Abstract
In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO), genetic algorithm (GA), firefly algorithm (FA) and optimal stopping rule (OSR) theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time) for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.
Highlights
With an exponential rise in energy demand, accompanied by the continuous decline in energy generation, an ongoing up gradation is required in today’s energy infrastructure
The simulations are done in Matlab and the short term forecasted load forecast information helps to take the decision which results in load reduction
We assume the real time pricing (RTP) remains constant during a month and change with fixed constant for the other months
Summary
With an exponential rise in energy demand, accompanied by the continuous decline in energy generation, an ongoing up gradation is required in today’s energy infrastructure. We provide the cost-effective energy optimization solution for the residential home energy management in order to attain multiple objectives such as minimum cost and maximum user comfort which incentivizes both the utility as well as the regular consumer. Demand response schemes work by shifting the load to off-peak hours from on-peak hours which minimizes the overall energy usage. The former motivates the consumer with price based and incentive-based schemes to restrain high peaks which help to maintain grid stability. The above-mentioned research study has explored the various aspects of residential home energy management Mostly their focus is on one of the objectives either on the cost minimization or the user comfort maximization.
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