scholarly journals Linearized Stochastic Scheduling of Interconnected Energy Hubs Considering Integrated Demand Response and Wind Uncertainty

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2448 ◽  
Author(s):  
Yining Zhang ◽  
Yubin He ◽  
Mingyu Yan ◽  
Chuangxin Guo ◽  
Yi Ding
Keyword(s):  
Natural Gas ◽  
Demand Response ◽  
Demand Curve ◽  
Stochastic Scheduling ◽  
Linearization Method ◽  
Piecewise Linearization ◽  
Interconnected System ◽  
Approximation Accuracy ◽  
Scheduling Model ◽  
Upper Level

In the context of the Energy Internet, customers are supplied by energy hubs (EH), while the EHs are interconnected through an upper-level transmission system. In this paper, a stochastic scheduling model is proposed for the interconnected EHs considering integrated demand response (DR) and wind variation. The whole integrated energy system (IES) is linearly modeled for the first time. The output-input relationship within the energy hub is denoted as a linearized matrix, while the upper-level power and natural gas transmission systems are analyzed through piecewise linearization method. A novel sequential linearization method is further proposed to balance computational efficiency and approximation accuracy. Integrated demand response is introduced to smooth out demand curve, considering both internal DR achieved by the optimal energy conversion strategy within energy hubs, and external DR achieved by demand adjustment on the customer’s side. Distributed energy storage like natural gas and heat storage are considered to provide buffer for system operation. The proposed stochastic model is solved by scenario-based optimization with a backward scenario reduction strategy. Numerical tests on a three-hub and seventeen-hub interconnected system that validates the effectiveness of the proposed scheduling model and solution methodology.

scholarly journals Day-Ahead and Intra-Day Collaborative Optimized Operation among Multiple Energy Stations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 936
Author(s):  
Jingjing Zhai ◽  
Xiaobei Wu ◽  
Zihao Li ◽  
Shaojie Zhu ◽  
Bo Yang ◽  
...  
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Programming Model ◽  
Energy System ◽  
Linearization Method ◽  
Mixed Integer ◽  
Operation Model ◽  
Piecewise Linearization ◽  
Constraints Network ◽  
Operation Scheduling

An integrated energy system (IES) shows great potential in reducing the terminal energy supply cost and improving energy efficiency, but the operation scheduling of an IES, especially integrated with inter-connected multiple energy stations, is rather complex since it is affected by various factors. Toward a comprehensive operation scheduling of multiple energy stations, in this paper, a day-ahead and intra-day collaborative operation model is proposed. The targeted IES consists of electricity, gas, and thermal systems. First, the energy flow and equipment composition of the IES are analyzed, and a detailed operation model of combined equipment and networks is established. Then, with the objective of minimizing the total expected operation cost, a robust optimization of day-ahead and intra-day scheduling for energy stations is constructed subject to equipment operation constraints, network constraints, and so on. The day-ahead operation provides start-up and shut-down scheduling of units, and in the operating day, the intra-day rolling operation optimizes the power output of equipment and demand response with newly evolved forecasting information. The photovoltaic (PV) uncertainty and electric load demand response are also incorporated into the optimization model. Eventually, with the piecewise linearization method, the formulated optimization model is converted to a mixed-integer linear programming model, which can be solved using off-the-shelf solvers. A case study on an IES with five energy stations verifies the effectiveness of the proposed day-ahead and intra-day collaborative robust operation strategy.

Applying multiple types of demand response to optimal day-ahead stochastic scheduling in the distribution network

2020 ◽  
Vol 14 (20) ◽  
pp. 4509-4519
Author(s):  
Jie Yang ◽  
Hongjun Gao ◽  
Shengyong Ye ◽  
Lin Lv ◽  
Youbo Liu ◽  
...  
Keyword(s):  
Demand Response ◽  
Distribution Network ◽  
Stochastic Scheduling

scholarly journals An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4398
Author(s):  
Yiqi Li ◽  
Jing Zhang ◽  
Zhoujun Ma ◽  
Yang Peng ◽  
Shuwen Zhao
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Value At Risk ◽  
Operating Cost ◽  
Optimization Method ◽  
Conditional Value At Risk ◽  
Demand Side ◽  
Scheduling Model ◽  
Management Optimization ◽  
Demand Side Response

With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers’ willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users’ willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department.

Methodology for the evaluation of demand response strategies for the management of natural gas systems

Energy ◽  
2021 ◽  
pp. 121283
Author(s):  
Lina Montuori ◽  
Manuel Alcázar-Ortega ◽  
Carlos Álvarez-Bel
Keyword(s):  
Natural Gas ◽  
Demand Response ◽  
Response Strategies

scholarly journals A Multiobjective Scheduling Optimization Model for Multienergy Complementary System Integrated by Wind-Photovoltaic-Convention Gas Turbines considering Demand Response

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Zhang Lihui ◽  
Xin He ◽  
Ju Liwei
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Gas Turbines ◽  
Risk Level ◽  
Objective Functions ◽  
Entropy Weight ◽  
Scheduling Optimization ◽  
Power Sources ◽  
Complementary System ◽  
Scheduling Model

To utilize the complementary feature of different power sources, wind power plant (WPP), and solar photovoltaic power (PV), convention gas turbines (CGT) and incentive-based demand response (IBDR) are integrated into a multienergy complementary system (MECS) with the implementation of price-based demand response (PBDR). Firstly, the power output model of WPP, PV, and CGT is constructed and the mathematical model of DR is presented. Then, a multiobjective scheduling model is proposed for MECS operation under the objective functions of the maximum economic benefit, the minimum abandoned energy, and the minimum risk level. Thirdly, the payoff table of objective functions is put forward for converting the multiobjective model into a single objective model by using entropy weight method to calculate weighting coefficients of different objective functions. Finally, the improved IEEE 30 bus system is taken as the simulation system with four simulation scenarios for comparatively analyzing the influence of PBDR and IBDR on MECS operation. The simulation results show the following: (1) The MECS fully utilized the complementarity of different power sources; CGT and IBDR can provide peaking service for WPP and PV to optimize overall system operation. (2) The proposed algorithm can solve the MECS multiobjective scheduling optimization model, and the system scheduling results in the comprehensive optimal mode can take into account different appeal. And the total revenue, abandoned energy capacity, and load fluctuation are, respectively, 108009.30¥, 11.62 MW h, and 9.74 MW. (3) PBDR and IBDR have significant synergistic optimization effects, which can promote the grid connection of WPP and PV. When they are both introduced, the peak-to-valley ratio of the load curve is 1.19, and the abandoned energy is 5.85 MW h. Therefore, the proposed MECS scheduling model and solution algorithm could provide the decision basis for decision makers based on their actual situation.

scholarly journals Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Smart Grids

2018 ◽  
Author(s):  
Joao Soares ◽  
Fernando Lezama ◽  
Bruno Canizes ◽  
M. Ali Fotouhi Ghazvini ◽  
Zita Vale ◽  
...  
Keyword(s):  
Smart Grids ◽  
Stochastic Scheduling ◽  
Scheduling Model
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