Various demand side management techniques and its role in smart grid–the state of art

The current lifestyle of humanity relies heavily on energy consumption, thus rendering it an inevitable need. An ever-increasing demand for energy has resulted from the increasing population. Most of this demand is met by the traditional sources that continuously deplete and raise significant environmental issues. The existing power structure of developing nations is aging, unstable, and unfeasible, further prolonging the problem. The existing electricity grid is unstable, vulnerable to blackouts and disruption, has high transmission losses, low quality of power, insufficient electricity supply, and discourages distributed energy sources from being incorporated. Mitigating these problems requires a complete redesign of the system of power distribution. The modernization of the electric grid, i.e., the smart grid, is an emerging combination of different technologies designed to bring about the electrical power grid that is changing dramatically. Demand side management (DSM) allow customers to be more involved in contributors to the power systems to achieve system goals by scheduling their shiftable load. Effective DSM systems require the participation of customers in the system that can be done in a fair system. This paper focuses primarily on techniques of DSM and demand responses (DR), including scheduling approaches and strategies for optimal savings.

Techno-Economic Evaluation of Hybrid Energy Systems Using Artificial Ecosystem-Based Optimization with Demand Side Management

Electrification of remote rural areas by adopting renewable energy technologies through the advancement of smart micro-grids is indispensable for the achievement of continuous development goals. Satisfying the electricity demand of consumers while adhering to reliability constraints with docile computation analysis is challenging for the optimal sizing of a Hybrid Energy System (HES). This study proposes the new application of an Artificial Ecosystem-based Optimization (AEO) algorithm for the optimal sizing of a HES while satisfying Loss of Power Supply Probability (LPSP) and Renewable Energy Fraction (REF) reliability indices. Furthermore, reduction of surplus energy is achieved by adopting Demand Side Management (DSM), which increases the utilization of renewable energy. By adopting DSM, 28.38%, 43.05%, and 65.37% were achieved for the Cost of Energy (COE) saving at 40%, 60%, and 80% REF, respectively. The simulation and optimization results demonstrate the most cost-competitive system configuration that is viable for remote-area utilization. The proposed AEO algorithm is further compared to Harris Hawk Optimization (HHO) and the Future Search Algorithm (FSA) for validation purpose. The obtained results demonstrate the efficacy of AEO to achieve the optimal sizing of HES with the lowest COE, the highest consistent level, and minimal standard deviation compared with HHO and FSA. The proposed model was developed and simulated using the MATLAB/code environment.

Optimizing the effect of charging electric vehicles on distribution transformer using demand side management

This paper mainly aims to present the demand side management (DSM) of electric vehicles (EVs) by considering distribution transformer capacity at a residential area. The objective functions are formulated to obtain charging schedule for individual owner by i) minimizing the load variance and ii) price indicated charging mechanism. Both the objective functions profit the owner in the following ways: i) fulfilling their needs, ii) minimizing overall charging cost, iii) lessening the peak load, and iv) avoiding the overloading of distribution transformer. The proposed objective functions were worked on a residential area with 8 houses each house with an EV connected to a 20 kVA distribution transformer. The formulations were tested in LINGO platform optimization modeling software for linear, nonlinear, and integer programming. The results obtained were compared which gives good insight of EV load scheduling without actual price prediction.

Clustering-Based Energy Management of Residential Loads by using Artificial Intelligence

Developing countries have witnessed a remarkable surge in the energy crisis due to the supply and demand gap. One of the solutions to overcome this problem is the optimal use of energy that can be achieved by employing demand side management (DSM) and demand response (DR) methods intelligently. Machine learning and data analysis tools help us create intelligent systems that motivate us to use machine learning to implement DSM/DR programs. In this paper, a novel DSM algorithm is introduced to implement DSM intelligently by using artificial intelligence. The results show an efficient implementation of an artificial neural network (ANN) along with demand side management, whereas the peak and off-peak loads were normalized to a certain range where a perfect agreement between supply and demand can be reached.

Blockchain-Enabled Energy Demand Side Management Cap and Trade Model

Global economic growth, demographic explosion, digitization, increased mobility, and greater demand for heating and cooling due to climate change in different world areas are the main drivers for the surge in energy demand. The increase in energy demand is the basis of economic challenges for power companies alongside several socio-economic problems in communities, such as energy poverty, defined as the insufficient coverage of energy needs, especially in the residential sector. Two main strategies are considered to meet this increased demand. The first strategy focuses on new sustainable and eco-friendly modes of power generation, such as renewable energy resources and distributed energy resources. The second strategy is demand-side oriented rather than the supply side. Demand-side management, demand response (DR), and energy efficiency (EE) programs fall under this category. On the other hand, the decentralization and digitization of the energy sector convoyed by the emersion of new technologies such as blockchain, Internet of Things (IoT), and Artificial Intelligence (AI), opened the door to new solutions for the energy demand dilemma. Among these technologies, blockchain has proved itself as a decentralized trading platform between untrusted peers without the involvement of a trusted third party. This newly introduced Peer-to-Peer (P2P) trading model can be used to create a new demand load control model. In this article, the concept of an energy cap and trade demand-side management (DSM) model is introduced and simulated. The introduced DSM model is based on the concept of capping consumers’ monthly energy consumption and rewarding consumers who do not exceed this cap with energy tradeable credits that can be traded using blockchain-based Peer-to-Peer (P2P) energy trading. A model based on 200 households is used to simulate the proposed DSM model and prove that this model can be beneficial to both energy companies and consumers.