scholarly journals Impact of Demand Response on Optimal Sizing of Distributed Generation and Customer Tariff

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 190
Author(s):  
Krishna Mohan Reddy Pothireddy ◽  
Sandeep Vuddanti ◽  
Surender Reddy Salkuti
Keyword(s):  
Energy Management ◽  
Distributed Generation ◽  
Demand Response ◽  
Distribution System ◽  
Fossil Fuels ◽  
Global Climate ◽  
Management Strategies ◽  
Communication Technologies ◽  
Load Demand ◽  
Drastic Increase

Due to the surge in load demand, the scarcity of fossil fuels, and increased concerns about global climate change, researchers have found distributed energy resources (DERs) to be alternatives to large conventional power generation. However, a drastic increase in the installation of distributed generation (DGs) increases the variability, volatility, and poor power quality issues in the microgrid (MG). To avoid prolonged outages in the distribution system, the implementation of energy management strategies (EMS) is necessary within the MG environment. The loads are allowed to participate in the energy management (EM) so as to reduce or shift their demands to non-peak hours such that the maximum peak in the system gets reduced. Therefore, this article addresses the complication of solutions, merits, and demerits that may be encountered in today’s power system and encompassed with demand response (DR) and its impacts in reducing the installation cost, the capital cost of DGs, and total electricity tariff. Moreover, the paper focuses on various communication technologies, load clustering techniques, and sizing methodologies presented.

scholarly journals Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 172
Author(s):  
Sunny Katyara ◽  
Muhammad Fawad Shaikh ◽  
Shoaib Shaikh ◽  
Zahid Hussain Khand ◽  
Lukasz Staszewski ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Fuzzy Inference System ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Management Strategies ◽  
Test System ◽  
Optimal Placement ◽  
Inference System ◽  
Simulated Environment

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

Strategic demand response framework for energy management in distribution system based on network loss sensitivity

2020 ◽  
Vol 31 (8) ◽  
pp. 1385-1402
Author(s):  
Sampath Kumar ◽  
M Sushama
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Distribution System ◽  
Network Performance ◽  
Test System ◽  
Test Cases ◽  
Energy Management System ◽  
Loss Minimization ◽  
Scheduling Strategy ◽  
Scheduling Strategies

This paper discusses an energy management system–based demand response scheduling strategy in distribution system. The proposed strategy includes customer payment minimization and network loss minimization as responsive load scheduling objectives through centralized approach. Two types of optimization strategies each based on payment minimization and network loss sensitivity are discussed in this paper. Thus, the proposed scheduling strategy can effectively resolve the optimality issue between different objectives of the distribution system scheduling under demand response penetration. The demand response scheduling strategies are simulated using standard IEEE 37 bus distribution test system through different cases of scheduling and optimization scenarios. The simulation results are presented, discussed, and compared with the base test cases without demand response penetration and without optimization strategies under demand response penetration to demonstrate the effectiveness of network loss, sensitivity consideration and optimization strategies in carrying out distribution system scheduling. In addition, sensitivity analysis is performed. The variation of distribution network performance is analyzed for various test cases and scenarios at different penetration levels.

scholarly journals Futuristic Sustainable Energy Management in Smart Environments: A Review of Peak Load Shaving and Demand Response Strategies, Challenges, and Opportunities

2020 ◽  
Vol 12 (14) ◽  
pp. 5561 ◽  
Author(s):  
Bhagya Nathali Silva ◽  
Murad Khan ◽  
Kijun Han
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Energy Demand ◽  
Management Strategies ◽  
Peak Load ◽  
Critical Discussion ◽  
Future Research ◽  
Smart Environments ◽  
Challenges And Opportunities ◽  
Management Approaches

The emergence of the Internet of Things (IoT) notion pioneered the implementation of various smart environments. Smart environments intelligibly accommodate inhabitants’ requirements. With rapid resource shrinkage, energy management has recently become an essential concern for all smart environments. Energy management aims to assure ecosystem sustainability, while benefiting both consumers and utility providers. Although energy management emerged as a solution that addresses challenges that arise with increasing energy demand and resource deterioration, further evolution and expansion are hindered due to technological, economical, and social barriers. This review aggregates energy management approaches in smart environments and extensively reviews a variety of recent literature reports on peak load shaving and demand response. Significant benefits and challenges of these energy management strategies were identified through the literature survey. Finally, a critical discussion summarizing trends and opportunities is given as a thread for future research.

scholarly journals Demand Response Management Algorithm for Distributed Multi-Utility Environment in Smart Grid

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Game Theory ◽  
Smart Grid ◽  
Information And Communication Technologies ◽  
Energy Management ◽  
Demand Response ◽  
Communication Technologies ◽  
Energy Market ◽  
Grid Environment ◽  
Utility Companies ◽  
Information And Communication

Effective usage of Information and Communication Technologies (ICT) has started with a paradigm shift in the energy management and functioning of the conventional power grid. It also aids in the maintenance of the complete information about consumer usage pattern, power storage, supply and regulation. Blending of information and communication technologies with energy management creates a smart grid environment which makes it move to the next horizon. The smart grid environment, uplifts renewable energy sources and brings out novel strategies in the energy market. The new functioning of the energy market attracts more utility companies for decentralized power generation and optimizes the power price for the consumer. The consumer plays an active role in the demand response modelling to maximize the welfare of the utility and to obtain the optimized price for their demand. In this paper, a novel demand response management scheme is proposed for multi-utility environment. The utility companies function in a peer to peer manner to communicate effectively and to select a specific utility from a set of utilities for the power supply. The selection of single utility is based on a non-cooperative game theory algorithm where the demand and generated power should be balanced to maximize the welfare of the utility and the residential consumers. The power price can be updated in an equal interval to allow all the utilities to participate in the Distributed Multi-Utility Demand Response Management (DMDRM) system. The simulated results justify that the distributed noncooperative game theory algorithm certainly maximizes the welfare of the utility companies and residential consumers.

scholarly journals Literature review of BESS implementation in DER

2019 ◽  
Vol 16 (2) ◽  
pp. 321-326
Author(s):  
Edwin Rivas Trujillo ◽  
Jesús M López Lezama ◽  
Tays Estefanía Gutiérrez Castro
Keyword(s):  
Energy Storage ◽  
Distributed Generation ◽  
Electric Vehicles ◽  
Demand Response ◽  
Distribution System ◽  
Energy Resources ◽  
Distributed Energy ◽  
Fundamental Part ◽  
Battery Energy Storage Systems ◽  
Battery Energy

Distributed Energy Resources (DER) have been a fundamental part of the inclusion of Battery Energy Storage Systems (BESS) in the generation and distribution system. This work shows an exhaustive review of the different approaches that the authors have developed when implementing BESS in DER, its scope and applications in different environments, observing that the most covered topics are Smart Grid (SG), Distributed Generation (DG), Energy Storage (ES) and where little information is found on the topics of Electric Vehicles (EV), Advanced Measurement (AM) and Demand Response (DR), this is to give an overview of the progress the authors have had and it allows to know in which field of application less information is found, facilitating the search for new researchers.

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