Comparative study of flexibility enhancement technologies for the coal-fired combined heat and power plant

Proposal and comprehensive analysis of gas-wind-photovoltaic-hydrogen integrated energy system considering multi-participant interest preference

This paper proposes a real-time pricing scheme for the demand response management between one energy provider and multiple energy hub operators. A promising energy trading scenario has been designed for the near future integrated energy system. The Stackelberg game approach was employed to capture the interactions between the energy provider (leader) and energy consumers (follower). A distributed algorithm was proposed to derive the Stackelberg equilibrium, then, the best strategies for the energy provider and each energy hub operator were explored in order to maximize their benefits. Simulation results showed that the proposed method can balance the energy supply and demand, improve the payoffs for all players, as well as smooth the aggregated load profiles of all energy consumers.

https://www.mdpi.com/1996-1073/11/10/2858/pdf

A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach

Integrated energy systems (IESs) are considered a trending solution for the energy crisis and environmental problems. However, the diversity of energy sources and the complexity of the IES have brought challenges to the economic operation of IESs. Aiming at achieving optimal scheduling of components, an IES operation optimization model including photovoltaic, combined heat and power generation system (CHP) and battery energy storage is developed in this paper. The goal of the optimization model is to minimize the operation cost under the system constraints. For the optimization process, an optimization principle is conducted, which achieves maximized utilization of photovoltaic by adjusting the controllable units such as energy storage and gas turbine, as well as taking into account the battery lifetime loss. In addition, an integrated energy system project is taken as a research case to validate the effectiveness of the model via the improved differential evolution algorithm (IDEA). The comparison between IDEA and a traditional differential evolution algorithm shows that IDEA could find the optimal solution faster, owing to the double variation differential strategy. The simulation results in three different battery states which show that the battery lifetime loss is an inevitable factor in the optimization model, and the optimized operation cost in 2016 drastically decreased compared with actual operation data.

Optimal Scheduling of Integrated Energy Systems with Combined Heat and Power Generation, Photovoltaic and Energy Storage Considering Battery Lifetime Loss

Recently, integrated energy systems have become a new type of energy supply model. It is clear that integrated energy systems can improve energy efficiency and reduce costs. However, the use of a battery energy storage system (BESS) as a backup power source will affect the operating costs of a regional integrated energy system (RIES) in different situations. In this paper, a regional integrated energy system including wind turbines, photovoltaics, gas turbines and battery energy storage was introduced. In order to obtain the minimum operation cost, an operation optimization model was built. The schedule plans of each unit were optimized by a moth flame optimization (MFO) algorithm. Finally, three different scenarios were proposed for the simulation optimization. The simulation optimization results show that when the BESS is used as a backup power source, the operating cost of the system and the resulting pollutant emissions are less than the diesel generator (DG) set. Therefore, it is worthwhile to use BESS instead of DG as the backup power source in RIES.

https://www.mdpi.com/1996-1073/11/11/2990/pdf

Research on Battery Energy Storage as Backup Power in the Operation Optimization of a Regional Integrated Energy System

Facing the growing problem of the curtailed wind during winter heating periods in the north of China, a flexible control method of wind power heating using the integrated energy system to consume the curtailed wind is proposed. Energy storage technology is introduced to coordinate regenerative electric boilers to match the fast fluctuation of wind power. The consuming model of the curtailed wind for the integrated energy system is established under three models through the study of the consumption mechanisms for the curtailed wind. The “wind power-energy storage system” and “wind power-regenerative electric boiler” are regarded as the game’s participants, and the maximum daily benefit of the system is regarded as the objective function, an economic evaluation model of the integrated energy system to consume the curtailed wind based on game theory is established. For comparative analysis, the model is respectively solved by particle swarm optimization (PSO) and the firefly algorithm (FA). The effectiveness of the model is verified by the actual operation data of a wind power heating demonstration project in China. The simulation results show that the most economy benefits of the overall system can be obtained by using energy storage systems to coordinate regenerative electric boilers, which provides a theoretical basis for the consumption of the curtailed wind.

Economic Evaluation of Actively Consuming Wind Power for an Integrated Energy System Based on Game Theory

Modular multilevel converter with supercapacitor (SC) packs-based energy storage system (MMC-SESS) can play a role in energy transition and renewable energy consumption. However, SESS's imbalance problem is caused by the inconsistency of its packs, which has a negative effect on the utilization of the MMC-SESS and has become a hot research topic. Confronting this problem, this paper proposes a state of charge (SOC) balancing control strategy amongst SC packs based on a discrete time-domain predictive power model in MMC-SESS based medium-voltage direct-current (MVDC). It is a double-loop control to accurately control the charging and discharging power. The outer power control establishes a discrete time-domain predictive power model for the SOC differences. The inner current loop designs a hybrid controller based on the continuous control set-model prediction control (CCS-MPC) and the feedback control strategy to accurately track the dynamic current reference. Simulations in PSCAD/EMTDC have shown that the proposed control can achieve SOC balancing amongst SC packs and improve the dynamic response as well as mitigate the SC pack current ripples. 

SOC Balancing Control Based on Predictive Power Model Amongst Supercapacitor Packs in MMC with Embedded Energy Storage System

As large-scale wind and photovoltaic power generation incorporating into the AC power grid, the impact of new energy fluctuation and uncertainty on the transient stability of power grid have attracted widespread attention among researchers and engineers in the power industry. It is necessary to improve the transient simulation and modelling ability of new energy to provide a more efficient simulation platform for the stability research of new energy generation connecting to large power grid. In this paper, the ADPSS technology of large-scale new energy electromagnetic transient simulation is mainly studied in depth, including the following contents: the design and development of the interface and program of ADPSS-Matlab co-simulation, the improvement and optimization of the parallel high performance computing, and the verification of the accuracy of co-simulation for the modelling and experimental research with large-scale new energy simulation.

Development and Design of ADPSS Co-simulation Technology for Large-Scale New Energy Electromagnetic Transient Simulation in High Performance Calculation

In view of the increasing proportion of renewable energy access in distribution network, the traditional programming method was unable to meet the needs of power grid development. A hierarchical and zoned energy-saving programming method for distribution network with high proportion of renewable energy was proposed. Firstly, the capacity and layout of high proportion renewable energy were optimized to realize the initial site selection and capacity determination of renewable energy. Secondly, taking the renewable energy planning results as the initial value, a bi-level iterative programming model was constructed to realize the optimization decision making model with cost and energy-saving benefits as the goal. Finally, the harmony search algorithm was used to solve the proposed optimization model. The real power grid data is used for analysis. The simulation results show the effectiveness and feasibility of the proposed model and method.

Energy Saving Programming of Distribution Network Considering High Permeability Renewable Energy Access

With the higher share of renewable energy, the flexibility of the power system needs to be improved. In recent years, the loads on the demand side can provide flexibility with the development of demand response technology. Thermostatically controlled loads (TCLs), as a kind of large-scale flexible load, have been a significant demand response resource. In this paper, a demand response strategy for TCLs considering consumer utility function is proposed. Firstly, a utility function is modeled to estimate the utility of TCLs. Then a model for estimating the potential of TCLs is built. On this foundation, the power system operators (PSOs) sort TCLs by consumer utility. TCLs with lower consumer utility will be called first. The study case adopts realistic data of an eastern city in China, which verifies the effectiveness of the proposed strategy.

Demand response strategy for TCLs considering consumer utility

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Feng Wang

Author Tools

Chat about Author