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

Author(s):  
Jianqiang Hu ◽  
Jinde Cao

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.

2021 ◽  
Vol 5 (4) ◽  
pp. 140
Author(s):  
Jianqiang Hu ◽  
Jinde Cao

Demand response (DR) flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a demand response optimization dispatch control strategy for flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs) with stochastic renewable power injection. Firstly, a chance constraint look-ahead programming model is proposed to maximize the social welfare of both units and load agents, through which the optimal power scheduling for 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. It has been shown that the proposed dispatch and control strategy can coordinate the flexible load agents and the renewable power injection. Finally, the simulation results on a modified IEEE 39 bus system demonstrate the effectiveness of the proposed demand response strategy.


Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1333 ◽  
Author(s):  
Diego Francisco Larios ◽  
Enrique Personal ◽  
Antonio Parejo ◽  
Sebastián García ◽  
Antonio García ◽  
...  

The complexity of power systems is rising mainly due to the expansion of renewable energy generation. Due to the enormous variability and uncertainty associated with these types of resources, they require sophisticated planning tools so that they can be used appropriately. In this sense, several tools for the simulation of renewable energy assets have been proposed. However, they are traditionally focused on the simulation of the generation process, leaving the operation of these systems in the background. Conversely, more expert SCADA operators for the management of renewable power plants are required, but their training is not an easy task. SCADA operation is usually complex, due to the wide set of information available. In this sense, simulation or co-simulation tools can clearly help to reduce the learning curve and improve their skills. Therefore, this paper proposes a useful simulator based on a JavaScript engine that can be easily connected to any renewable SCADAs, making it possible to perform different simulated scenarios for novel operator training, as if it were a real facility. Using this tool, the administrators can easily program those scenarios allowing them to sort out the lack of support found in setting up facilities and training of novel operator tasks. Additionally, different renewable energy generation models that can be implemented in the proposed simulator are described. Later, as a use example of this tool, a study case is also performed. It proposes three different wind farm generation facility models, based on different turbine models: one with the essential generation turbine function obtained from the manufacturer curve, another with an empirical model using monotonic splines, and the last one adding the most important operational states, making it possible to demonstrate the usefulness of the proposed simulation tool.


Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5771
Author(s):  
Yumiko Iwafune ◽  
Kazuhiko Ogimoto

The increase in the number of electric vehicles (EVs) has led to increased global expectations that the application of this technology may result in the reduction of CO2 emissions through the replacement of conventional petrol vehicles and ensure the flexibility of power systems such as batteries. In this paper, we propose a residential demand response (DR) evaluation model that considers the degradation mechanism of the EV battery and examines the effective battery operation. We adopted the already-proposed NiMnCo battery degradation model to develop an EV DR evaluation model. In this model, the battery operation is optimized to minimize the electricity and degradation costs affected by ambient temperature, battery state of charge (SOC), and depth of discharge. In this study, we evaluated the impact of the relevant parameters on the economics of the DR of EV batteries for 10 all-electric detached houses with photovoltaic system assuming multiple EV driving patterns and battery (dis)charging constraints. The results indicated that the degradation costs are greatly affected by the SOC condition. If a low SOC can be managed with a DR strategy, the total cost can be reduced. This is because the sum of the reduction of purchased cost from the utility and calendar degradation costs are higher than the increase of the cycle degradation cost. In addition, an analysis was conducted considering different driving patterns. The results showed that the cost reduction was highest when a driving pattern was employed in which the mileage was low and the staying at home time was large. When degradation costs are included, the value of optimized charging and discharging operations is more apparent than when degradation costs are not considered.


2021 ◽  
Vol 13 (11) ◽  
pp. 5792
Author(s):  
Mahdi Azimian ◽  
Vahid Amir ◽  
Reza Habibifar ◽  
Hessam Golmohamadi

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.


Author(s):  
Mojtaba Najafi ◽  
Samaneh Ahmadi ◽  
Masoud Dashtdar

Abstract Determining the optimal reserve in power systems is closely related to uncertainties in power generation and risks of outage of supply to consumers. Distributed generation sources such as wind farms are usual reasons for uncertainties in MW production. This uncertainty can be alleviated by providing enough reserve in which demand response (DR) programs can play role of resources for reserve. In an electricity market structure, the mentioned points are usually handled by Independent System Operator (ISO) in energy and reserve markets. This paper deals with the problem of reliability-based reserve management. In the mentioned problem, the DR program in the form of interruptible loads is also considered. A new method is proposed in which ISO settle energy and reserve markets simultaneously while employing the DR in the first stage. In addition, consumers’ requirements of reliability are included by assuming that they have possibility to offer their desired levels of reliability to the ISO. The amount of reserve obtained from market settlement is adjusted based on the different reliability requirements of the consumers and different scenarios of the wind farm operation, in the second stage of the proposed method. Also the cost of the reserve adjustment is fairly allocated to producers and consumers. The proposed method applies stochastic programming formulation and its validity is assessed by the GAMS software. Simulation results show that how amount and cost of reserve could be adjusted to cover power balance, cost of power production and load interruption and required reliability of consumers.


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