scholarly journals Probabilistic Optimization of Networked Multi-Carrier Microgrids to Enhance Resilience Leveraging Demand Response Programs

2021 ◽  
Vol 13 (11) ◽  
pp. 5792
Author(s):  
Mahdi Azimian ◽  
Vahid Amir ◽  
Reza Habibifar ◽  
Hessam Golmohamadi
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Electricity Market ◽  
Energy Systems ◽  
Probabilistic Optimization ◽  
Renewable Power ◽  
Key Factor ◽  
Load Curtailment ◽  
Local Supply ◽  
Demand Response Programs

Microgrids have emerged as a practical solution to improve the power system resilience against unpredicted failures and power outages. Microgrids offer substantial benefits for customers through the local supply of domestic demands as well as reducing curtailment during possible disruptions. Furthermore, the interdependency of natural gas and power networks is a key factor in energy systems’ resilience during critical hours. This paper suggests a probabilistic optimization of networked multi-carrier microgrids (NMCMG), addressing the uncertainties associated with thermal and electrical demands, renewable power generation, and the electricity market. The approach aims to minimize the NMCMG costs associated with the operation, maintenance, CO2e emission, startup and shutdown cost of units, incentive and penalty payments, as well as load curtailment during unpredicted failures. Moreover, two types of demand response programs (DRPs), including time-based and incentive-based DRPs, are addressed. The DRPs unlock the flexibility potentials of domestic demands to compensate for the power shortage during critical hours. The heat-power dual dependency characteristic of combined heat and power systems as a substantial technology in microgrids is considered in the model. The simulation results confirm that the suggested NMCMG not only integrates the flexibility potentials into the microgrids but also enhances the resilience of the energy systems.

scholarly journals Optimal robust operation of combined heat and power systems with demand response programs

2019 ◽  
Vol 149 ◽  
pp. 1359-1369 ◽  
Author(s):  
Majid Majidi ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Combined Heat And Power ◽  
Demand Response Programs

scholarly journals Demand Response Programs in Multi-Energy Systems: A Review

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4332
Author(s):  
Morteza Vahid-Ghavidel ◽  
Mohammad Sadegh Javadi ◽  
Matthew Gough ◽  
Sérgio F. Santos ◽  
Miadreza Shafie-khah ◽  
...  
Keyword(s):  
Energy Storage ◽  
Demand Response ◽  
Storage System ◽  
Energy Systems ◽  
Energy Storage System ◽  
Consumer Participation ◽  
Diverse Range ◽  
Good Communication ◽  
Demand Response Programs ◽  
The Impact

A key challenge for future energy systems is how to minimize the effects of employing demand response (DR) programs on the consumer. There exists a diverse range of consumers with a variety of types of loads, such as must-run loads, and this can reduce the impact of consumer participation in DR programs. Multi-energy systems (MES) can solve this issue and have the capability to reduce any discomfort faced by all types of consumers who are willing to participate in the DRPs. In this paper, the most recent implementations of DR frameworks in the MESs are comprehensively reviewed. The DR modelling approach in such energy systems is investigated and the main contributions of each of these works are included. Notably, the amount of research in MES has rapidly increased in recent years. The majority of the reviewed works consider power, heat and gas systems within the MES. Over three-quarters of the papers investigated consider some form of energy storage system, which shows how important having efficient, cost-effective and reliable energy storage systems will be in the future. In addition, a vast majority of the works also considered some form of demand response programs in their model. This points to the need to make participating in the energy market easier for consumers, as well as the importance of good communication between generators, system operators, and consumers. Moreover, the emerging topics within the area of MES are investigated using a bibliometric analysis to provide insight to other researchers in this area.

scholarly journals Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1413 ◽  
Author(s):  
Arsalan Najafi ◽  
Mousa Marzband ◽  
Behnam Mohamadi-Ivatloo ◽  
Javier Contreras ◽  
Mahdi Pourakbari-Kasmaei ◽  
...  
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Wind Turbine ◽  
Demand Response ◽  
Electricity Market ◽  
Programming Model ◽  
Decision Making Process ◽  
Market Prices ◽  
Gas Network ◽  
Energy Hub

Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.

scholarly journals Chance-Constrained Optimization Dispatch and Demand Response Control of Thermostatically Controlled Loads and Plug-in Electric Vehicles

2021 ◽  
Author(s):  
Jianqiang Hu ◽  
Jinde Cao
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Demand Response ◽  
Wind Farm ◽  
Sample Average Approximation ◽  
Chance Constraint ◽  
Response Control ◽  
Renewable Power ◽  
Look Ahead ◽  
Thermostatically Controlled Loads

Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.

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