scholarly journals Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

2020 ◽  
Vol 10 (5) ◽  
pp. 1565
Author(s):  
Hang Yu ◽  
Zhiyuan Liu ◽  
Chaoen Li ◽  
Rui Liu
Keyword(s):  
Demand Response ◽  
Electricity Market ◽  
Optimal Solution ◽  
Energy System ◽  
Benefit Sharing ◽  
Selling Price ◽  
Pricing Mechanism ◽  
Integrated Energy System ◽  
The Impact

With the opening of the Chinese electricity market, as a retailer that provides energy services to consumers, the park-integrated energy system (PIES) not only serves as an effective way to earn benefits and reduce carbon emissions but also impacts the energy consumption characteristics of consumers. The PIES implements this function by adjusting the energy selling price in free energy markets. The pricing mechanism model (P-M model) is established to obtain the energy selling price in the planning and design stages. In this model, the impact of the demand response on the energy configuration and the impact of the changes in energy configuration on the energy cost price are both considered. Additionally, the optimal result ensures that both the consumers and the PIES benefit simultaneously. The reactive demand response zone, which represents a consumer trap, is found in numerical studies. The results indicate the following: (1) from the perspective of P-M model optimization, the benefit exclusive point of the PIES is the optimal solution in the short term; (2) from the perspective of the long-term benefit, the ultimate result in the relationship between the PIES and consumers is that the PIES will share its profits with consumers; in other words, benefit sharing point is the optimal solution for the long term.

scholarly journals Medium- and Long-Term Integrated Demand Response of Integrated Energy System Based on System Dynamics

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 710 ◽  
Author(s):  
Shuhui Ren ◽  
Xun Dou ◽  
Zhen Wang ◽  
Jun Wang ◽  
Xiangyan Wang
Keyword(s):  
Energy Storage ◽  
System Dynamics ◽  
Electric Vehicles ◽  
Demand Response ◽  
Energy System ◽  
Energy Utilization ◽  
Response Model ◽  
Integrated Energy System ◽  
Flexible Load

For the integrated energy system of coupling electrical, cool and heat energy and gas and other forms of energy, the medium- and long-term integrated demand response of flexible load, energy storage and electric vehicles and other demand side resources is studied. It is helpful to mine the potentials of demand response of various energy sources in the medium- and long-term, stimulate the flexibility of integrated energy system, and improve the efficiency of energy utilization. Firstly, based on system dynamics, the response mode of demand response resources is analyzed from different time dimensions, and the long-term, medium-term and short-term behaviors of users participating in integrated demand response are considered comprehensively. An integrated demand response model based on medium-and long-term time dimension is established. Then the integrated demand response model of integrated energy system scheduling and flexible load, energy storage and electric vehicles as the main participants is established to simulate the response income of users participating in the integrated demand response project, and to provide data sources for the medium- and long-term integrated demand response system dynamics model. Finally, an example is given to analyze the differences in response behaviors of flexible load, energy storage and electric vehicle users in different time dimensions under the conditions of policy subsidy, regional location and user energy preferences in different stages of the integrated energy system.

scholarly journals Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2539
Author(s):  
Zhengjie Li ◽  
Zhisheng Zhang
Keyword(s):  
Wind Power ◽  
Demand Response ◽  
Solar Power ◽  
Load Forecasting ◽  
Energy System ◽  
Optimal Scheduling ◽  
Scheduling Scheme ◽  
Load Power ◽  
Integrated Energy System ◽  
Operation Cost

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

scholarly journals A Hybrid Energy System Workflow for Energy Portfolio Optimization

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4392
Author(s):  
Jia Zhou ◽  
Hany Abdel-Khalik ◽  
Paul Talbot ◽  
Cristian Rabiti
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Electricity Market ◽  
Process Model ◽  
Net Present Value ◽  
Historical Data ◽  
Energy System ◽  
Initial Guess ◽  
Reduced Order ◽  
The Impact

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

scholarly journals Security Analysis of Regional Integrated Energy System Integrating Energy Storage

2021 ◽  
Vol 257 ◽  
pp. 01006
Author(s):  
Kaicheng Liu ◽  
Ying Guo ◽  
Dan Wang ◽  
Dezhi Li ◽  
Guixiong He
Keyword(s):  
Energy Storage ◽  
Security Analysis ◽  
System Security ◽  
Energy System ◽  
Improve Energy Efficiency ◽  
Energy Hub ◽  
Integrated Energy System ◽  
Security Region ◽  
The Impact ◽  
Practical Security

Regional integrated energy system (RIES) can realize multi-energy conversion and complementation so as to improve energy efficiency, which also brings more security risks. The regional integrated energy system security region (RIESSR) is a security analysis method to describe the safe area for the operating points of RIES based on the N-1 guideline. As a controllable device, energy storage (ES) which is installed in the energy hub (EH) plays an important role in improving system security. Therefore, this paper establishes the model of practical security region of RIES integrating energy storage, and studies the impact of ES on total supply capability (TSC) and practical security boundary. Finally, a specific case is set to simulate and verify the model. By comparing the scenario with ES and the scenario without ES, it can be seen that the solution result of TSC increases and the security region extends across quadrant when the RIES is integrated with ES system. The capacity and location of the ES also impact on TSC and RIESSR.

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