Optimal management of home loads with renewable energy integration and demand response strategy

Highlights

• Effects of DSM and renewable energy integration on energy saving of households are investigated.

• Home energy management model consisting of microgrid and demand response was proposed.

• Loads were shifted based on two price-schemes using BPSO algorithm in MATLAB.

• DSM implementation along with renewable resources produced significant trade-off.

Abstract

The implementation of proper energy management techniques and utilization of renewable energy resources enhance the energy efficiency and stability of future grid systems. This research proposed a home energy management model consisting of microgrid framework and demand side management (DSM) technique. To reduce peak load, peak to average, and energy cost, households’ loads were shifted on the basis of price-based tariff such as flexible and time of use tariff. Simulation was carried out using binary particle swarm optimization algorithm in MATLAB. The microgrid was mathematically modeled, and the impacts of DSM integrated microgrid were analysed for different households in terms of electricity cost reduction. Simulations suggested that DSM implementation significantly reduced peak loads and renewable resources produced trade-off. Renewable energy integration with DSM can be a promising approach for the significant reduction in total electricity cost of households by paying less for purchasing electricity and selling the surplus electricity to the grid.

Introduction

The increase in energy consumption as well as the rapid growth of population and the lack of implementation of proper management techniques result in an extreme spike in energy demand [1,2]. Existing electrical grid worsens the situation due to their old-fashioned design as well as redundant and overstressed infrastructure [3]. Recently environmental pollution gains much attention among the scientists and environmentalists because of public consciousness of reducing carbon emission and political pressure [4]. About 85% of the total global energy consumption depends on fossil fuels [5]. The excessive usage of fossil fuels is associated with the release of substantial CO₂ emission. The integration of renewable energy resources (RES) in power generation is the most effective and feasible way to promote sustainable development and reduce environmental pollution [6].

The utilization and optimization of RES lead to the concept of microgrid as a replacement for fossil energy sources [7]. Recently, the use of microgrid system has gained significant popularity in finding ways to increase the stability of energy supply by integrating distributed energy resources, such as wind turbines and solar panels, and distributed energy storage like batteries [8,9]. Moreover, microgrid has distinct features, including reliability, low investment costs, and regulations of different distributed generator units’ output voltage and current [10]. Additionally, microgrid can be incorporated with different demand side management (DSM) frameworks and operated in grid connected and off-grid modes.

DSM refers to the amendments of consumers’ energy consumption pattern to enhance the efficiency of electrical energy systems and network [[11], [12], [13], [14]]. This technique modifies daily energy consumption pattern to achieve a desired load profile [[15], [16], [17]]. Numerous scholars studied the implementation of DSM in residential loads management [[18], [19], [20]]. For instance, Gottwalt et al. carried out a simulation for shifting residential loads on the basis of time of use (TOU) tariff [21]. Özkan also developed DSM model to reduce peak load and energy cost for residential area [22]. Bharathi et al. solved DSM optimization problems for residential loads management using genetic algorithm to reduce peak load [23]. Srinivasan et al. developed a game theory based dynamic pricing model for residential and commercial electricity sector in Singapore [24].

Renewable energy integration or microgrid has gained significant attention in recent years because of the economic and environmental benefits of renewable energy [[25], [26], [27], [28]]. Additionally, the modeling of microgrid with the application of DSM can play a pivotal role in reducing peak load, energy inefficiency, and operational cost of electricity provider, thus helping to reduce carbon footprints. This integrated approach can reduce the amount of energy required to buy from the grid. Recent works were dedicated to the implementation of DSM along with the integration of renewable energy for achieving a balance between energy generation and consumption. Quiggin et al. implemented the demand response program in a residential microgrid with the integration of solar photovoltaic, wind turbine, and energy storage [29]. This method optimized the energy balance between supply and demand by reducing peak demand fluctuations by 16%, thus significantly reducing CO₂. Palma-Behnke et al. modeled a microgrid system with solar photovoltaic, wind turbine, diesel generator, battery bank, and water supply system [30]. The DSM mechanism along with artificial neural network was used for determining optimal operation and reducing costs. Shen et al. carried out an incentive-based demand response program in a microgrid, which consisted of micro turbines, wind turbine, fuel cells, solar photovoltaic, storage devices, and controllable load [31]. The authors solved an operational scheduling problem to achieve an optimal scheduling of the batteries and diesel generators. Nunna and Doolla proposed an intelligent energy management system with demand response program to reduce peak load demand in the microgrid, and they performed simulation by using Java Agent Development framework [32]. To reduce electricity cost, an incentive method was recommended by the authors to motivate the consumers for participating in the demand response program. Philippou et al. investigated a price-based DSM mechanism and performed sensitivity analysis to reduce peak load during summer and winter [33].

The state of the art review, discussed above suggests that a wide number of literatures have focussed on the demand response using various algorithms; however studies related to different price-based demand response using BPSO and consideration of uncertainty in renewable generation integrated with demand response are very limited in the literature. Therefore, this research work proposes an integrated framework for home energy management, which includes load scheduling based on different tariff rate and fulfilling the scheduled demand from renewables. The major contributions of this research work are as follows: A DSM system is modeled based on flexible and time of use tariff rate to minimize the households’ energy cost and the optimization problem was solved using BPSO algorithm with sensitivity analysis. A microgrid was modeled considering uncertainty of renewables generation and the effect of renewable generation on scheduled load was studied with surplus power selling. For this purpose, a comprehensive case study was carried out for scheduling households’ load and evaluating different situations over weekend and weekday.