Probabilistic Supply and Demand Balance Control Method based on Electricity Market Mechanism Considering N-1 Criterion

2021 ◽  
Vol 141 (1) ◽  
pp. 1-12
Author(s):  
Akira Koide ◽  
Takao Tsuji ◽  
Kazuyuki Tanaka ◽  
Hitoshi Sugimoto
Keyword(s):  
Electricity Market ◽  
Control Method ◽  
Balance Control ◽  
Supply And Demand ◽  
Market Mechanism

scholarly journals IoT-enabled Microgrid for Intelligent Energy-aware Buildings: A Novel Hierarchical Self-consumption Scheme with Renewables

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 550 ◽  
Author(s):  
Yanpeng Wu ◽  
Ying Wu ◽  
Josep Guerrero ◽  
Juan Vasquez ◽  
Emilio Palacios-García ◽  
...  
Keyword(s):  
Renewable Energy ◽  
State Transition ◽  
Control Method ◽  
Balance Control ◽  
Supply And Demand ◽  
Building Blocks ◽  
Energy Aware ◽  
Layered Architecture ◽  
Intelligent Buildings ◽  
Transition Mechanism

This paper presents a novel hierarchical Internet of Things (IoT)-based scheme for Microgrid-Enabled Intelligent Buildings to achieve energy digitalization and automation with a renewable energy self-consumption strategy. Firstly, a hierarchical structure of Microgrid-Enabled Intelligent Buildings is designed to establish a two-dimensional fusion layered architecture for the microgrid to interact with the composite loads of buildings. The building blocks and functions of each layer are defined specifically. Secondly, to achieve transparent information fusion and interactive cooperation between the supply-side and demand-side, a state transition mechanism driven by a combination of time and events is proposed to activate the real-time and mutual response of generation and loads dynamically. Thirdly, based on the above hierarchical fusion structure and data-driven state transition mechanism, a power balance control algorithm driven by a self-consumption strategy is further proposed to achieve the autonomous balance of supply and demand. Finally, the IoT Microgrid Laboratory at Aalborg University is introduced to show how to implement this novel hierarchical IoT-based scheme in a Microgrid-Enabled Intelligent Building, and the power consensus control method based on the state transition mechanism is verified to achieve a renewable energy self-consumption strategy.

scholarly journals Research on Funding Differences among Regional Electricity Markets in Northern China based on Thermoelectric Decoupling

2020 ◽  
Vol 185 ◽  
pp. 01017
Author(s):  
Sen Wang ◽  
Can Sun ◽  
Zhiyong Gan ◽  
Liansheng Zhou ◽  
Guilin Wang ◽  
...  
Keyword(s):  
Market Power ◽  
Electricity Markets ◽  
Electricity Market ◽  
Supply And Demand ◽  
Market Price ◽  
Northern China ◽  
Spot Market ◽  
Market Mechanism ◽  
Large Influence ◽  
Electricity Spot Market

With the development of China’s electricity spot market, planned power and market power will coexist for a long time. At the same time, by avoiding the risk of market price fluctuation through medium and long-term market, spot market guarantees electricity balance and secure operation of the grid. The electricity market mechanism has an increasingly large influence on the operation and dispatching model of power system. In spot market, decoupling operation model of market and non-market power has a large influence on both supply and demand sides and improper dredging mechanism may cause significant settlement deviation. To solve this problem, the paper, taking a city in northern China as an example, analyzes the electricity spot market, compares the sources of difference fund of market and non-market power under decoupling and non-decoupling models and compares the pros and cons of coupling and decoupling. The paper also studies the disparity of difference fund and proposes advice adapted to the electricity spot market development of northern China.

Research on a New Type of Lithium Battery String Equalization and Management System

2011 ◽  
Vol 383-390 ◽  
pp. 1470-1476
Author(s):  
Hao Wang ◽  
Ding Guo Shao ◽  
Lu Xu
Keyword(s):  
Lithium Batteries ◽  
Management System ◽  
Lithium Battery ◽  
Large Scale ◽  
Control Method ◽  
Balance Control ◽  
Industrial Applications ◽  
Control Technique ◽  
New Type ◽  
Management Functions

Lithium battery has been employed widely in many industrial applications. Parameter mismatches between lithium batteries along a series string is the critical limits of the large-scale applications in high power situation. Maintaining equalization between batteries is the key technique in lithium batteries application. This paper summarizes normal equalization techniques and proposed a new type of lithium Battery Equalization and Management System (BEMS) employing the isolated DC-DC converter structure. The system is integrated both equalization functions and management functions by using distributed 3-level controlled structure and digital control technique. With this control method the flexibility of the balance control strategy and the compatibility for different battery strings are both improved dramatically. The experimental results show optimizing equalization, efficiency and the battery string life span has been extended.

Robust Control for Urban Living Water System with Seasonal Volatility

2013 ◽  
Vol 295-298 ◽  
pp. 1927-1930
Author(s):  
Ke Bai Li
Keyword(s):  
Water Management ◽  
Robust Control ◽  
Control Method ◽  
Supply And Demand ◽  
Water System ◽  
Management Model ◽  
State Variables ◽  
Urban Living ◽  
Target Value ◽  
Seasonal Volatility

Established urban living water management model. With capital and labor as state variables, using the pole assignment robust control method, realize the urban living water system supply and demand balance tending to target value.

Energy Management System for Hybrid Microgrids

2021 ◽  
Vol 69 (2) ◽  
pp. 21-30
Author(s):  
Nasreddine ATTOU ◽  
Sid-Ahmed ZIDI ◽  
Mohamed KHATIR ◽  
Samir HADJERI
Keyword(s):  
Energy Management ◽  
Electricity Market ◽  
Management System ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Supply And Demand ◽  
Energy Sources ◽  
Variable Load ◽  
Energy Management System ◽  
Price Variation

Energy management in grid-connected Micro-grids (MG) has undergone rapid evolution in recent times due to several factors such as environmental issues, increasing energy demand and the opening of the electricity market. The Energy Management System (EMS) allows the optimal scheduling of energy resources and energy storage systems in MG in order to maintain the balance between supply and demand at low cost. The aim is to minimize peaks and fluctuations in the load and production profile on the one hand, and, on the other hand, to make the most of renewable energy sources and energy exchanges with the utility grid. In this paper, our attention has been focused on a Rule-based energy management system (RB EMS) applied to a residential multi-source grid-connected MG. A Microgrid model has been implemented that combines distributed energy sources (PV, WT, BESS), a number of EVs equipped with the Vehicle to Grid technology (V2G) and variable load. Different operational scenarios were developed to see the behaviour of the implemented management system during the day, including the random demand profile of EV users, the variation in load and production, grid electricity price variation. The simulation results presented in this paper demonstrate the efficacy of the suggested EMS and confirm the strategy's feasibility as well as its ability to properly share power among different sources, loads and vehicles by obeying constraints on each element.

scholarly journals Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 43 ◽  
Author(s):  
Mesbaholdin Salami ◽  
Farzad Movahedi Sobhani ◽  
Mohammad Ghazizadeh
Keyword(s):  
Neural Networks ◽  
Electricity Market ◽  
Simulation Optimization ◽  
Supply And Demand ◽  
Electricity Supply ◽  
Wavelet Neural Networks ◽  
Short Term ◽  
Swarm Optimization ◽  
Forecasting Accuracy ◽  
Short Term Forecasting

The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

scholarly journals An Experimental Analysis of Price Formation on the Polish Power Exchange

2015 ◽  
Vol 15 (2) ◽  
pp. 115-127
Author(s):  
Ewa Drabik
Keyword(s):  
Electricity Market ◽  
Market Price ◽  
Market Mechanism ◽  
Financial Supervision ◽  
Energy Market ◽  
Price Support ◽  
Transaction Prices ◽  
Power Exchange ◽  
Energy Trade ◽  
And Control

Abstract The Polish energy market gained its competitive character in late 1990s. At that time in majority of European countries a new law was enacted (in Poland – in 1987), which enabled the creation of internal energy markets. The Polish Power Exchange has been functioning since the end of 1999. However, from the very onset it has constituted a vital component of under grounding liberalization of electricity market. Since it was created the Polish Power Exchange has served as a market mechanism for setting objective energy market price. Support and control of the Polish Financial Supervision Authority guarantee the security of concluded transactions. The spot energy market was created as the first one and has functioned according to the rule of the double auction. The model of Sadrieh will be used for the description of the auction rules applied to the spot energy trade on the Polish Power Exchange. Furthermore, an algorithm on the basis of which it is possible to forecast transaction prices is presented. The effectiveness of this algorithm will be compared with other traditional methods of forecasting transaction prices.

scholarly journals Non-smooth analysis in a national electricity market model: a complex approach

Memorias ◽  
2018 ◽  
pp. 58-66
Author(s):  
Johnny Valencia ◽  
Gerard Olivar ◽  
Johan Manuel Redondo ◽  
Danny Ibarra Vega ◽  
Carlos Peña Rincón
Keyword(s):  
System Dynamics ◽  
Electricity Market ◽  
Equilibrium Points ◽  
Supply And Demand ◽  
Market Model ◽  
National Network ◽  
Demand Growth ◽  
Zero Rate ◽  
National Electricity Market ◽  
Business Approach

In this paper, we show the preliminary results in a proposed a model for the supply and demand of electricity in a domestic market based on system dynamics. Additionally, the model indicates piecewise smooth differential equations arising from the diagram of flows and levels, using dynamical systems theory for the study of the stability of the equilibrium points that have such a system. A bifurcation analysis approach is proposed to define and understand the complex behavior. Until now, no work has been reported related to this topic using bifurcations criteria. The growing interest in personal ways of self-generation using renewable sources can lead the national grid to a standstill and low investment in the system. However, it is essential to preserve the national network as a power supply support to domestic and enterprise demand. To understand this scenario, we include an analysis of zero-rate demand growth. Under this hypothesis, a none smooth bifurcation appears related to a policy which involves the variation of the capacity charge. As a first significant result, we found that it is possible to preserve the investments in the market since, through the capacity charge parameter, the system dynamics can be controlled. Then, from a business approach, it is necessary to know the effects of the capacity charge as the strategic policy in the system generation price scheme.

scholarly journals Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
J. M. Torres ◽  
R. M. Aguilar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Electricity Market ◽  
Power Plants ◽  
Wind Farm ◽  
Polynomial Regression ◽  
Supply And Demand ◽  
Feedforward Neural Networks ◽  
Activation Function ◽  
Renewable Energies

Making every component of an electrical system work in unison is being made more challenging by the increasing number of renewable energies used, the electrical output of which is difficult to determine beforehand. In Spain, the daily electricity market opens with a 12-hour lead time, where the supply and demand expected for the following 24 hours are presented. When estimating the generation, energy sources like nuclear are highly stable, while peaking power plants can be run as necessary. Renewable energies, however, which should eventually replace peakers insofar as possible, are reliant on meteorological conditions. In this paper we propose using different deep-learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market, by making predictions 12 and 36 hours in advance. We develop and compare various estimators based on feedforward, convolutional, and recurrent neural networks. These estimators were trained and validated with data from a wind farm located on the island of Tenerife. We show that the best candidates for each type are more precise than the reference estimator and the polynomial regression currently used at the wind farm. We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.

Sign in / Sign up

Export Citation Format

Share Document