scholarly journals Dynamic Optimization of Combined Cooling, Heating, and Power Systems with Energy Storage Units

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2288 ◽  
Author(s):  
Jiyuan Kuang ◽  
Chenghui Zhang ◽  
Fan Li ◽  
Bo Sun
Keyword(s):  
Power Systems ◽  
Operating Cost ◽  
Variable Cost ◽  
State Variables ◽  
Energy Supply System ◽  
Constant Cost ◽  
Cost Calculations ◽  
Operation Schedule ◽  
Short Time ◽  
Combined Cooling

In this paper, a combined cooling, heating, and power (CCHP) system with thermal storage tanks is introduced. Considering the plants’ off-design performance, an efficient methodology is introduced to determine the most economical operation schedule. The complex CCHP system’s state transition equation is extracted by selecting the stored cooling and heating energy as the discretized state variables. Referring to the concept of variable cost and constant cost, repeated computations are saved in phase operating cost calculations. Therefore, the most economical operation schedule is obtained by employing a dynamic solving framework in an extremely short time. The simulation results indicated that the optimized operating cost is reduced by 40.8% compared to the traditional energy supply system.

Impacts of equipment off-design characteristics on the optimal design and operation of combined cooling, heating and power systems

2013 ◽  
Vol 48 ◽  
pp. 40-47 ◽  
Author(s):  
Zhe Zhou ◽  
Pei Liu ◽  
Zheng Li ◽  
Efstratios N. Pistikopoulos ◽  
Michael C. Georgiadis
Keyword(s):  
Optimal Design ◽  
Power Systems ◽  
Design Characteristics ◽  
Combined Cooling

scholarly journals Reliability and Maintenance Prioritization Analysis of Combined Cooling, Heating and Power Systems

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1519 ◽  
Author(s):  
Jinming Jiang ◽  
Xindong Wei ◽  
Weijun Gao ◽  
Soichiro Kuroki ◽  
Zhonghui Liu
Keyword(s):  
Power Systems ◽  
Combined Cooling

scholarly journals State Estimation in Electric Power Systems Leveraging Graph Neural Networks

2022 ◽  
Author(s):  
Ognjen Kundacina ◽  
Mirsad Cosovic ◽  
Dejan Vukobratovic
Keyword(s):  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Electric Power Systems ◽  
State Variables ◽  
Evaluation Phase ◽  
Measurement Units ◽  
Synthetic Datasets ◽  
Graph Neural Networks ◽  
Test Scenarios

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling rates. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

scholarly journals Multiobjective Electrical Power Dispatch of Thermal Units with Convex and Non-Convex Fuel Cost Functions for 24 Hours Load Demands

2020 ◽  
Vol 9 (3) ◽  
pp. 1534-1542
Keyword(s):  
Power Systems ◽  
Electrical Power ◽  
Economic Dispatch ◽  
Operating Cost ◽  
Load Condition ◽  
Minimum Cost ◽  
Electricity Sector ◽  
Operating Characteristics ◽  
Total Load ◽  
Gaseous Emission

There are a host of difficult issues with scheduling, operation, and control of integrated power systems. The electricity sector is changing rapidly, and one of the most important concerns is deciding operational strategies to meet electricity demand. It is a greater challenge to satisfy customer demand for power at a minimum cost. The operating characteristics of all generators may be different. In general, operating cost is not proportionate to the performance of these generators. Therefore challenge for power utilities to balance the total load between generators. For a specific load condition on energy systems, Economic Dispatch(ED) seeks to reduce the fuel costs of power generation units. Moreover, energy utilities have also an important task to reduce gaseous emission. So the ED problem can be recognized as a complicated multi-objective optimization problem (MOOP) with two competing targets, the minimal cost of fuel and the minimum emissions effects. This paper presented an efficient method, hybrid of particle swarm optimization (PSO) and a learning-based optimization (TLBO) for combined environmental issues because of gaseous emission and economic dispatch (CEED) problems. The results were shown and verified by PSO and TLBO for standard 3 and 6-generator frameworks with combined issues of emission and economic dispatch taking into account line losses and prohibited zones (POZs) on hourly demand for 24 hours

Application of Meta-Heuristic Optimization Algorithms in Electric Power Systems

2015 ◽  
pp. 1292-1341
Author(s):  
N.I. Voropai ◽  
A. Z. Gamm ◽  
A. M. Glazunova ◽  
P. V. Etingov ◽  
I. N. Kolosok ◽  
...  
Keyword(s):  
Power Systems ◽  
Electric Power ◽  
Optimization Algorithms ◽  
Main Tool ◽  
Electric Power Systems ◽  
Heuristic Optimization ◽  
State Variables ◽  
State Equations ◽  
Electric Power Supply ◽  
Linear And Nonlinear Programming

Optimization of solutions on expansion of electric power systems (EPS) and their control plays a crucial part in ensuring efficiency of the power industry, reliability of electric power supply to consumers and power quality. Until recently, this goal was accomplished by applying classical and modern methods of linear and nonlinear programming. In some complicated cases, however, these methods turn out to be rather inefficient. Meta-heuristic optimization algorithms often make it possible to successfully cope with arising difficulties. State estimation (SE) is used to calculate current operating conditions of EPS using the SCADA measurements of state variables (voltages, currents etc.). To solve the SE problem, the Energy Systems Institute of Siberian Branch of Russian Academy of Sciences (ESI of SB RAS) has devised a method based on test equations (TE), i.e. on the steady state equations that contain only measured parameters. Here, a technique for EPS SE using genetic algorithms (GA) is suggested. SE is the main tool for EPS monitoring. The quality of SE results determines largely the EPS control efficiency. An algorithm for exclusion of wrong SE calculations is described. The algorithm using artificial neural networks (ANN) is based on the analysis of results of the calculation performed solving the SE problem with different combinations of constants. The proposed procedure is checked on real data.

Combined Cooling, Heating, and Power Systems

2017 ◽  
Author(s):  
Yang Shi ◽  
Mingxi Liu ◽  
Fang Fang
Keyword(s):  
Power Systems ◽  
Combined Cooling
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