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Keywords = price-based demand response (PBDR)

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25 pages, 5163 KiB  
Article
Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization
by Suchitra Dayalan, Sheikh Suhaib Gul, Rajarajeswari Rathinam, George Fernandez Savari, Shady H. E. Abdel Aleem, Mohamed A. Mohamed and Ziad M. Ali
Sustainability 2022, 14(17), 10985; https://doi.org/10.3390/su141710985 - 2 Sep 2022
Cited by 26 | Viewed by 2829
Abstract
Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce [...] Read more.
Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce their demand. This work aims to implement the IBDR strategy from the perspective of the service provider and consumers. The relationship between the different entities concerned is modelled. The incentives offered by the service provider (SP) to its consumers and the consumers’ reduced demand are optimized using Stackelberg–particle swarm optimization (SPSO) as a bi-level problem. Furthermore, the system with a grid operator, the industrial consumers of the grid operator, the service provider and its consumers are analyzed from the service provider’s viewpoint as a tri-level problem. The benefits offered by the service provider to its customers, the incentives provided by the grid operator to its industrial customers, the reduction of customer demand, and the average cost procured by the grid operator are optimized using SPSO and compared with the Stackelberg-distributed algorithm. The problem was analyzed for an hour and 24 h in the MATLAB environment. Besides this, sensitivity analysis and payment analysis were carried out in order to delve into the impact of the demand response program concerning the change in customer parameters. Full article
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19 pages, 3459 KiB  
Article
Low-Carbon Economic Dispatch Based on a CCPP-P2G Virtual Power Plant Considering Carbon Trading and Green Certificates
by Qingyou Yan, Xingbei Ai and Jinmeng Li
Sustainability 2021, 13(22), 12423; https://doi.org/10.3390/su132212423 - 10 Nov 2021
Cited by 49 | Viewed by 3476
Abstract
To improve the economic benefits of power systems in the process of achieving multi-energy complementation and decarbonization, this paper proposes a dispatching optimization model for virtual power plants (VPP) that considers carbon trading and green certificates. Firstly, the structure of the VPP system [...] Read more.
To improve the economic benefits of power systems in the process of achieving multi-energy complementation and decarbonization, this paper proposes a dispatching optimization model for virtual power plants (VPP) that considers carbon trading and green certificates. Firstly, the structure of the VPP system integrating wind and solar generators (WP and PV), power-to-gas (P2G), carbon capture power plants (CCPP) and price-based demand response (PBDR) is established. Secondly, the two-way interactive trading models among the VPP, carbon trading and green certification market are constructed. Then, the dispatching optimization model of the VPP is constructed. Finally, the numerical example is solved and analyzed by the chaotic particle swarm optimization algorithm, which verifies the rationality and effectiveness of the new model. The results show that: (1) when the VPP considers the CCPP-P2G, the cost of the system is reduced by USD 2550.48, while the CO2 emissions are reduced by nearly 50%; (2) the addition of PBDR reduces the CO2 emissions of the thermal power unit, which has reduced the cost of carbon tax by nearly 27.8%, further reducing the cost of the VPP; (3) the introduction of the carbon trading and green certificate market has reduced the operating cost of the VPP by nearly 22.24%. Full article
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16 pages, 5668 KiB  
Article
Price Based Demand Response for Optimal Frequency Stabilization in ORC Solar Thermal Based Isolated Hybrid Microgrid under Salp Swarm Technique
by Abdul Latif, Manidipa Paul, Dulal Chandra Das, S. M. Suhail Hussain and Taha Selim Ustun
Electronics 2020, 9(12), 2209; https://doi.org/10.3390/electronics9122209 - 21 Dec 2020
Cited by 58 | Viewed by 3220
Abstract
Smart grid technology enables active participation of the consumers to reschedule their energy consumption through demand response (DR). The price-based program in demand response indirectly induces consumers to dynamically vary their energy use patterns following different electricity prices. In this paper, a real-time [...] Read more.
Smart grid technology enables active participation of the consumers to reschedule their energy consumption through demand response (DR). The price-based program in demand response indirectly induces consumers to dynamically vary their energy use patterns following different electricity prices. In this paper, a real-time price (RTP)-based demand response scheme is proposed for thermostatically controllable loads (TCLs) that contribute to a large portion of residential loads, such as air conditioners, refrigerators and heaters. Wind turbine generator (WTG) systems, solar thermal power systems (STPSs), diesel engine generators (DEGs), fuel cells (FCs) and aqua electrolyzers (AEs) are employed in a hybrid microgrid system to investigate the contribution of price-based demand response (PBDR) in frequency control. Simulation results show that the load frequency control scheme with dynamic PBDR improves the system’s stability and encourages economic operation of the system at both the consumer and generation level. Performance comparison of the genetic algorithm (GA) and salp swarm algorithm (SSA)-based controllers (proportional-integral (PI) or proportional integral derivative (PID)) is performed, and the hybrid energy system model with demand response shows the supremacy of SSA in terms of minimization of peak load and enhanced frequency stabilization of the system. Full article
(This article belongs to the Section Power Electronics)
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26 pages, 4462 KiB  
Article
A Day-ahead and Day-in Decision Model Considering the Uncertainty of Multiple Kinds of Demand Response
by Siqing Sheng and Qing Gu
Energies 2019, 12(9), 1711; https://doi.org/10.3390/en12091711 - 6 May 2019
Cited by 10 | Viewed by 2446
Abstract
The uncertainty of demand response (DR) will affect the economics of power grid dispatch due to the randomness of participating users’ intentions. According to the different working mechanisms of price-based demand response (PBDR) and incentive-based demand response (IBDR), the uncertainty models of two [...] Read more.
The uncertainty of demand response (DR) will affect the economics of power grid dispatch due to the randomness of participating users’ intentions. According to the different working mechanisms of price-based demand response (PBDR) and incentive-based demand response (IBDR), the uncertainty models of two types of DR were established, respectively. Firstly, the fuzzy variable was used to describe the load change in PBDR, and the robust optimization theory was used to establish the uncertain set of the actual interruption of the interruptible load (IL). Secondly, according to the different acting speed of the two types of DR, they were deployed in the two-stage scheduling model with other output resources; then based on the fuzzy chance constrained programming theory and multi-stage robust optimization theory, the dispatch problem was transformed and solved by the bat algorithm (BA) and the entropy weighting method. Consequently, intraday running costs decrease with increasing confidence of day-ahead, but increase with day-in reliability, and the economics of the model were verified in the improved IEEE33 node system. Full article
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28 pages, 5403 KiB  
Article
A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism
by Yao Wang, Yan Lu, Liwei Ju, Ting Wang, Qingkun Tan, Jiawei Wang and Zhongfu Tan
Energies 2019, 12(3), 425; https://doi.org/10.3390/en12030425 - 29 Jan 2019
Cited by 16 | Viewed by 3140
Abstract
In order to meet the user’s electricity demand and make full use of distributed energy, a hybrid energy system (HES) was proposed and designed, including wind turbines (WTs), photovoltaic (PV) power generation, conventional gas turbines (CGTs), incentive-based demand response (IBDR), combined heat and [...] Read more.
In order to meet the user’s electricity demand and make full use of distributed energy, a hybrid energy system (HES) was proposed and designed, including wind turbines (WTs), photovoltaic (PV) power generation, conventional gas turbines (CGTs), incentive-based demand response (IBDR), combined heat and power (CHP) and regenerative electric (RE) boilers. Then, the collaborative operation problem of HES is discussed. First, the paper describes the HES’ basic structure and presents the output model of power sources and heating sources. Next, the maximum operating income and minimum load fluctuation are taken as the objective function, and a multi-objective model of HES scheduling is proposed. Then an algorithm for solving the model is proposed that comprises two steps: processing the objective functions and constraints into linear equations and determining the optimal weight of the objective functions. The selected simulation system is a microgrid located on an eastern island of China to comparatively analyze the influence of RE-heating storage (RE-HS) and price-based demand response (PBDR) on HES operation in relation to four cases. By analyzing the results, the following three conclusions are drawn: (1) HES can comprehensively utilize a variety of distributed energy sources to meet load demand. In particular, RE technology can convert the abandoned energy of WT and PV into heat during the valley load time, to meet the load demand combined with CHP; (2) The proposed multi-objective scheduling model of HES operation not only considers the maximum operating income but also considers the minimum load fluctuation, thus achieving the optimal balancing operation; (3) RE-HS and PBDR have a synergistic optimization effect, and when RE-HS and PBDR are both applied, an HES can achieve optimal operation results. Overall, the proposed decision method is highly effective and applicable, and decision makers could utilize this method to design an optimal HES operation strategy according to their own actual conditions. Full article
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28 pages, 2673 KiB  
Article
A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses
by Liwei Ju, Peng Li, Qinliang Tan, Zhongfu Tan and GejiriFu De
Energies 2018, 11(11), 2903; https://doi.org/10.3390/en11112903 - 25 Oct 2018
Cited by 16 | Viewed by 3671
Abstract
To make full use of distributed energy resources to meet load demand, this study aggregated wind power plants (WPPs), photovoltaic power generation (PV), small hydropower stations (SHSs), energy storage systems (ESSs), conventional gas turbines (CGTs) and incentive-based demand responses (IBDRs) into a virtual [...] Read more.
To make full use of distributed energy resources to meet load demand, this study aggregated wind power plants (WPPs), photovoltaic power generation (PV), small hydropower stations (SHSs), energy storage systems (ESSs), conventional gas turbines (CGTs) and incentive-based demand responses (IBDRs) into a virtual power plant (VPP) with price-based demand response (PBDR). Firstly, a basic scheduling model for the VPP was proposed in this study with the objective of the maximum operation revenue. Secondly, a risk aversion model for the VPP was constructed based on the conditional value at risk (CVaR) method and robust optimization theory considering the operating risk from WPP and PV. Thirdly, a solution methodology was constructed and three cases were considered for comparative analyses. Finally, an independent micro-grid on an industrial park in East China was utilized for an example analysis. The results show the following: (1) the proposed risk aversion scheduling model could cope with the uncertainty risk via a reasonable confidence degree β and robust coefficient Γ. When Γ ≤ 0.85 or Γ ≥ 0.95, a small uncertainty brought great risk, indicating that the risk attitude of the decision maker will affect the scheduling scheme of the VPP, and the decision maker belongs to the risk extreme aversion type. When Γ (0.85, 0.95), the decision-making scheme was in a stable state, the growth of β lead to the increase of CVaR, but the magnitude was not large. When the prediction error e was higher, the value of CVaR increased more when Γ increased by the same magnitude, which indicates that a lower prediction accuracy will amplify the uncertainty risk. (2) when the capacity ratio of (WPP, PV): ESS was higher than 1.5:1 and the peak-to-valley price gap was higher than 3:1, the values of revenue, VaR, and CVaR changed slower, indicating that both ESS and PBDR can improve the operating revenue, but the capacity scale of ESS and the peak-valley price gap need to be set properly, considering both economic benefits and operating risks. Therefore, the proposed risk aversion model could maximize the utilization of clean energy to obtain higher economic benefits while rationally controlling risks and provide reliable decision support for developing optimal operation plans for the VPP. Full article
(This article belongs to the Special Issue Operation and Control of Power Distribution Systems)
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