The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users’ energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches

Reinforcement learning for demand response: A review of algorithms and modeling techniques

As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest in the literature recently, especially for residential areas. As a new type of consumer load in the electric power system, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options instead of peak power procurement from the grid. In this paper, as the main contribution to the literature, a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized. A mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided for this purpose. A distributed small-scale renewable energy generation system, the V2H and V2G capabilities of an EV together with two-way energy trading of ESS, and different DR strategies are all combined in a single HEM system for the first time in the literature. The impacts of different EV owner consumer preferences together with the availability of ESS and two-way energy trading capabilities on the reduction of total electricity prices are examined with case studies.

Smart Household Operation Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR

Renewable energy is clean sources and has a much lower environmental impact than other energy sources. In Turkey, solar energy investments have been developed rapidly in recent years. Site selection for solar farms is a critical issue for large investments because of quality of terrain, local weathering factors, proximity to high transmission capacity lines, agricultural facilities and environmental conservation issues. Multi criteria evaluation methods are often used for different site selection studies. The purpose of this study was to determine suitable site selection for solar farms by using GIS and AHP in the study area. The final index model was grouped into four categories as “low suitable”, “moderate”, “suitable” and “best suitable” with an equal interval classification method. As a result, 15.38% (928.18 km 2 ) of the study area has low suitable, 14.38% (867.83 km 2 ) has moderate suitable, 15.98% (964.39 km 2 ) has suitable and 13.92% (840.07 km 2 ) has best suitable for solar farms area. 40.34% (2434.52 km 2 ) of the study area is not suitable for solar farm areas.

GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey

One of the most challenging problems associated with operation of smart micro-grids is the optimal energy management of residential buildings with respect to multiple and often conflicting objectives. In this paper, a multiobjective mixed integer nonlinear programming model is developed for optimal energy use in a smart home, considering a meaningful balance between energy saving and a comfortable lifestyle. Thorough incorporation of a mixed objective function under different system constraints and user preferences, the proposed algorithm could not only reduce the domestic energy usage and utility bills, but also ensure an optimal task scheduling and a thermal comfort zone for the inhabitants. To verify the efficiency and robustness of the proposed algorithm, a number of simulations were performed under different scenarios using real data, and the obtained results were compared in terms of total energy consumption cost, users' convenience rates, and thermal comfort level.

Optimal Smart Home Energy Management Considering Energy Saving and a Comfortable Lifestyle

The optimization of energy consumption, with consequent costs reduction, is one of the main challenges in present and future smart grids. Of course, this has to occur keeping the living comfort for the end-user unchanged. In this work, an approach based on the mixed-integer linear programming paradigm, which is able to provide an optimal solution in terms of tasks power consumption and management of renewable resources, is developed. The proposed algorithm yields an optimal task scheduling under dynamic electrical constraints, while simultaneously ensuring the thermal comfort according to the user needs. On purpose, a suitable thermal model based on heat-pump usage has been considered in the framework. Some computer simulations using real data have been performed, and obtained results confirm the efficiency and robustness of the algorithm, also in terms of achievable cost savings.

Optimal Home Energy Management Under Dynamic Electrical and Thermal Constraints

https://isiarticles.com/bundles/Article/pre/pdf/25884.pdf

Action dependent heuristic dynamic programming for home energy resource scheduling

The employment of intelligent energy management systems likely allows reducing consumptions and thus saving money for consumers. The residential load demand must be met, and some advantages can be obtained if specific optimization policies are taken. With an efficient use of renewable sources and power imported from the grid, an intelligent and adaptive system which manages the battery is able to satisfy the load demand and minimize the entire energy cost related to the scenario under study. In this paper, an adaptive dynamic programming-based algorithm is presented to face dynamic situations, in which some conditions of the environment or habits of customer may vary with time, especially using renewable energy. Based on the idea of smart grid, we propose an intelligent management scheme for renewable resources combined with battery implemented with a faster and simpler scheme of dynamic programming, by considering only one critic network and some optimization policies in order to satisfy the load demand. Since this kind of problem is suitable to avoid the training of an action network, the training loop among the two neural networks is deleted and the training process is greatly simplified. Computer simulations confirm the effectiveness of this self-learning design in a typical residential scenario.

Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management

Smart Home Energy Management is a very hot topic for the scientific community and some interesting solutions have also recently appeared on the market. One key issue is represented by the capability of planning the usage of energy resources in order to reduce the overall energy costs. This means that, considering the dynamic electricity price and the availability of adequately sized storage system, the expert system is supposed to automatically decide the more convenient policy for energy management from and towards the grid. In this work a comparison among different linear and nonlinear methods for home energy resource scheduling is proposed, considering the presence of data uncertainty into account. Indeed, whereas the employment of advanced optimization frameworks can take advantage by their inherent offline approach, the need to forecast the energy price and the amount of self-generated power. A residential scenario, in which a system storage and renewable resources are available and exploitable to match the user load demand, has been considered for performed computer simulations: obtained results show how the offline approaches provide good performance also in presence of uncertain data.

Optimization algorithms for home energy resource scheduling in presence of data uncertainty

In this paper an optimal controller for battery management in smart home environments is presented in order to save costs and minimize energy waste. The considered scenario includes a load profile that must always be satisfied, a battery-system that is able to storage electrical energy, a photovoltaic (PV) panel, and the main grid that is used when it is necessary to satisfy the load requirements or charge the battery. The optimal controller design is based on a class of adaptive critic designs (ACDs) called action dependent heuristic dynamic programming (ADHDP). Results obtained with this scheme outperform the ones obtained by using the particle swarm optimization (PSO) method.

Matteo Boaro

Papers

Optimal Home Energy Management Under Dynamic Electrical and Thermal Constraints

Action dependent heuristic dynamic programming for home energy resource scheduling

Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management

Optimization algorithms for home energy resource scheduling in presence of data uncertainty

Optimal battery management with ADHDP in smart home environments