scholarly journals Multi-Objective Optimal Scheduling Method for a Grid-Connected Redundant Residential Microgrid

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 296 ◽  
Author(s):  
Weiliang Liu ◽  
Changliang Liu ◽  
Yongjun Lin ◽  
Kang Bai ◽  
Liangyu Ma ◽  
...  
Keyword(s):  
Cooling System ◽  
Storage System ◽  
Operating Cost ◽  
Energy Storage System ◽  
Comfort Level ◽  
Optimal Scheduling ◽  
Mixed Integer ◽  
Nsga Ii ◽  
Multi Objective ◽  
Scheduling Method

Optimal scheduling of a redundant residential microgrid (RR-microgrid) could yield economical savings and reduce the emission of pollutants while ensuring the comfort level of users. This paper proposes a novel multi-objective optimal scheduling method for a grid-connected RR-microgrid in which the heating/cooling system of the RR-microgrid is treated as a virtual energy storage system (VESS). An optimization model for grid-connected RR-microgrid scheduling is established based on mixed-integer nonlinear programming (MINLP), which takes the operating cost (OC), thermal comfort level (TCL), and pollution emission (PE) as the optimization objectives. The non-dominate sorting genetic algorithm II (NSGA-II) is employed to search the Pareto front and the best scheduling scheme is determined by the analytic hierarchy process (AHP) method. In a case study, two kinds of heating/cooling systems, the radiant floor heating/cooling system (RFHCS) and the convection heating/cooling system (CHCS) are investigated for the RR-microgrid. respectively, and the feasibility and validity of the scheduling method are ascertained.

scholarly journals A New Power Sharing Scheme of Multiple Microgrids and an Iterative Pairing-Based Scheduling Method

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1605 ◽  
Author(s):  
Hong-Chao Gao ◽  
Joon-Ho Choi ◽  
Sang-Yun Yun ◽  
Seon-Ju Ahn
Keyword(s):  
Scheduling Algorithm ◽  
Storage System ◽  
Operating Cost ◽  
Optimization Method ◽  
Energy Storage System ◽  
Power Sharing ◽  
Mixed Integer ◽  
Total System ◽  
Peak Demand ◽  
Sharing Scheme

As the numbers of microgrids (MGs) and prosumers are increasing, many research efforts are proposing various power sharing schemes for multiple MGs (MMGs). Power sharing between MMGs can reduce the investment and operating costs of MGs. However, since MGs exchange power through distribution lines, this may have an adverse effect on the utility, such as an increase in peak demand, and cause local overcurrent issues. Therefore, this paper proposes a power sharing scheme that is beneficial to both MGs and the utility. This research assumes that in an MG, the energy storage system (ESS) is the major controllable resource. In the proposed power sharing scheme, an MG that sends power should discharge at least as much power from the ESS as the power it sends to other MGs, in order to actually decrease the total system demand. With these assumptions, methods for determining the power sharing schedule are proposed. Firstly, a mixed integer linear programming (MILP)-based centralized approach is proposed. Although this can provide the optimal power sharing solution, in practice, this method is very difficult to apply, due to the large calculation burden. To overcome the significant calculation burden of the centralized optimization method, a new method for determining the power sharing schedule is proposed. In this approach, the amount of power sharing is assumed to be a multiple of a unit amount, and the final power sharing schedule is determined by iteratively finding the best MG pair that exchange this unit amount. Simulation with a five MG scenario is used to test the proposed power sharing scheme and the scheduling algorithm in terms of a reduction in the operating cost of MGs, the peak demand of utility, and the calculation burden. In addition, the interrelationship between power sharing and the system loss is analyzed when MGs exchange power through the utility network.

Thermal Unit Commitment Problem with Wind Power and Energy Storage System

2013 ◽  
Vol 347-350 ◽  
pp. 1455-1461 ◽  
Author(s):  
Rui Wang ◽  
Yu Guang Xie ◽  
Kai Xie ◽  
Ya Qiao Luo
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Branch And Bound ◽  
Unit Commitment ◽  
Storage System ◽  
Operating Cost ◽  
Peak Load ◽  
Energy Storage System ◽  
Mixed Integer ◽  
Unit Commitment Problem

This paper presents a methodology for solving unit commitment (UC) problem for thermal units integrated with wind power and generalized energy storage system (ESS).The ESS is introduced to achieve peak load shaving and reduce the operating cost. The volatility of wind power is simulated by multiple scenarios, which are generated by Latin hypercube sampling. Meanwhile, the scenario reduction technique based on probability metric is introduced to reduce the number of scenarios so that the computational burden can be alleviated. The thermal UC problem with volatile wind power and ESS is transformed to a deterministic optimization which is formulated as the mixed-integer convex program optimized by branch and bound-interior point method. During the branch and bound process, the best first search and depth first search are combined to expedite the computation. The effectiveness of the proposed algorithm is demonstrated by a ten unit UC problem.

scholarly journals Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1124 ◽  
Author(s):  
Byeong-Cheol Jeong ◽  
Dong-Hwan Shin ◽  
Jae-Beom Im ◽  
Jae-Young Park ◽  
Young-Jin Kim
Keyword(s):  
Energy Storage ◽  
Scheduling Algorithm ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Operating Cost ◽  
Optimal Schedule ◽  
Energy Storage System ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Actual Case

Optimal operation scheduling of energy storage systems (ESSs) has been considered as an effective way to cope with uncertainties arising in modern grid operation such as the inherent intermittency of the renewable energy sources (RESs) and load variations. This paper proposes a scheduling algorithm where ESS power inputs are optimally determined to minimize the microgrid (MG) operation cost. The proposed algorithm consists of two stages. In the first stage, hourly schedules during a day are optimized one day in advance with the objective of minimizing the operating cost. In the second stage, the optimal schedule obtained from the first stage is repeatedly updated every 5 min during the day of operation to compensate for the uncertainties in load demand and RES output power. The ESS model is developed considering operating efficiencies and then incorporated in mixed integer linear programming (MILP). Penalty functions are also considered to acquire feasible optimal solutions even under large forecasting errors in RES generation and load variation. The proposed algorithm is verified in a campus MG, implemented using ESSs and photovoltaic (PV) arrays. The field test results are obtained using open-source software and then compared with those acquired using commercial software.

scholarly journals MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1898 ◽  
Author(s):  
Rae-Kyun Kim ◽  
Mark B. Glick ◽  
Keith R. Olson ◽  
Yun-Su Kim
Keyword(s):  
Storage System ◽  
Pso Algorithm ◽  
Energy Storage System ◽  
Optimal Scheduling ◽  
Mixed Integer ◽  
Minimum Problem ◽  
Diesel Generator ◽  
Constraint Violation ◽  
Local Minimum Problem ◽  
Better Than

This paper presents the optimal scheduling of a diesel generator and an energy storage system (ESS) while using a detailed battery ESS energy efficiency model. Optimal scheduling has been hampered to date by the nonlinearity and complexity of the battery ESS. This is due to the battery ESS efficiency being a multiplication of inverter and battery efficiency and the dependency of an inverter and any associated battery efficiencies on load and charging and discharging. We propose a combined mixed-integer linear programming and particle swarm optimization (MILP-PSO) algorithm as a novel means of addressing these considerations. In the algorithm, MILP is used to find some initial points of PSO, so that it can find better solution. Moreover, some additional algorithms are added into PSO to modify and, hence, improve its ability of dealing with constraint conditions and the local minimum problem. The simulation results show that the proposed algorithm performs better than MILP and PSO alone for the practical microgrid. The results also indicated that simplification or neglect of ESS efficiency when applying MILP to scheduling may cause a constraint violation.

scholarly journals Small Cogeneration Unit with Heat and Electricity Storage

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2102
Author(s):  
Josef Stetina ◽  
Michael Bohm ◽  
Michal Brezina
Keyword(s):  
Internal Combustion Engine ◽  
Cooling System ◽  
Combustion Engine ◽  
Catalytic Converter ◽  
Storage System ◽  
Internal Combustion ◽  
Energy Storage System ◽  
Balance Model ◽  
Exhaust Pipe ◽  
Water Storage Tank

A micro cogeneration unit based on a three-cylinder internal combustion engine, Skoda MPI 1.0 L compressed natural gas (CNG), with an output of 25 kW at 3000 RPM is proposed in this paper. It is a relatively simple engine, which is already adopted by the manufacturer to operate on CNG. The engine life and design correspond to the original purpose of use in the vehicle. A detailed dynamic model was created in the GT-SUITE environment and implemented into an energy balance model that includes its internal combustion engine, heat exchangers, generator, battery storage, and water storage tank. The 1D internal combustion engine model provides us with information on engine start-up time, actual effective power, friction power, and the amount of heat going to the cooling system and exhaust pipe. The catalytic converter was removed from the exhaust pipe, and the engine was always operating at full load; thus, engine power control is not considered. An energy storage system for an island operation of the entire power unit for a large, detached house was designed to withstand accumulated energy for a few days in the case of a breakout. To reach a low initial system cost, the possible implementation of worn-out battery packs toward emission reduction in terms of the second life of the battery is proposed. The energy and emission balance are carried out, and the service life of the engine is also discussed.

scholarly journals Multi-Objective Optimization of a Battery-Supercapacitor Hybrid Energy Storage System Based on the Concept of Cyber-Physical System

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1801
Author(s):  
Chenyun Pan ◽  
Shengyu Tao ◽  
Hongtao Fan ◽  
Mengyao Shu ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Cyber Physical System ◽  
Multi Objective Optimization ◽  
Hybrid Energy ◽  
Multi Objective ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
Control Scheme

Optimal operation of energy storage systems plays an important role in enhancing their lifetime and efficiency. This paper combines the concepts of the cyber–physical system (CPS) and multi-objective optimization into the control structure of the hybrid energy storage system (HESS). Owing to the time-varying characteristics of HESS, combining real-time data with physical models via CPS can significantly promote the performance of HESS. The multi-objective optimization model designed in this paper can improve the utilization of supercapacitors, reduce energy consumption, and prevent the state of charge (SOC) of HESS from exceeding the limitation. The new control scheme takes the characteristics of the components of HESS into account and is beneficial in reducing battery short-term power cycling and high discharge currents. The rain-flow counting algorithm is applied for battery life prediction to quantify the benefits of the HESS under the control scheme proposed. A much better power-sharing relationship between the supercapacitor and the lithium–ion battery (LiB) can be observed from the SIMULINK results and the case study with our new control scheme. Moreover, compared to the traditional low-pass filter control method, the battery lifetime is quantifiably increased from 3.51 years to 10.20 years while the energy efficiency is improved by 1.56%.

scholarly journals Reduction of Power Imbalances Using Battery Energy Storage System in a Bulk Power System with Extremely Large Photovoltaics Interactions

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 522
Author(s):  
Rajitha Udawalpola ◽  
Taisuke Masuta ◽  
Taisei Yoshioka ◽  
Kohei Takahashi ◽  
Hideaki Ohtake
Keyword(s):  
Energy Storage ◽  
Power System ◽  
Solar Irradiance ◽  
Storage System ◽  
Energy Storage System ◽  
Mixed Integer ◽  
Battery Energy Storage System ◽  
Large Power ◽  
Battery Energy Storage ◽  
Battery Energy

Power imbalances such as power shortfalls and photovoltaic (PV) curtailments have become a major problem in conventional power systems due to the introduction of renewable energy sources. There can be large power shortfalls and PV curtailments because of PV forecasting errors. These imbalances might increase when installed PV capacity increases. This study proposes a new scheduling method to reduce power shortfalls and PV curtailments in a PV integrated large power system with a battery energy storage system (BESS). The model of the Kanto area, which is about 30% of Japan’s power usage with 60 GW grid capacity, is used in simulations. The effect of large PV power integration of 50 GW and 100 GW together with large BESS capacity of 100 GWh and 200 GWh has been studied. Mixed integer linear programming technique is used to calculate generator unit commitment and BESS charging and discharging schedules. The simulation results are shown for two months with high and low solar irradiance, which include days with large PV over forecast and under forecast errors. The results reveal that the proposed method eliminates power shortfalls by 100% with the BESS and reduce the PV curtailments by 69.5% and 95.2% for the months with high and low solar irradiance, respectively, when 200 GWh BESS and 100 GW PV power generation are installed.

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