Optimal simultaneous day-ahead scheduling and hourly reconfiguration of distribution systems considering responsive loads

被引:68
作者
Esmaeili, Saeid [1 ]
Anvari-Moghaddam, Amjad [2 ]
Jadid, Shahram [1 ]
Guerrero, Josep M. [2 ]
机构
[1] Iran Univ Sci & Technol, Dept Elect Engn, Tehran, Iran
[2] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark
关键词
Day-ahead scheduling; Demand response; Hourly reconfiguration; Particle swarm optimization; Smart distribution system; DEMAND RESPONSE; NETWORK RECONFIGURATION; ELECTRIC VEHICLES; ENERGY MANAGEMENT; LOSS MINIMIZATION; ALGORITHM; OPTIMIZATION;
D O I
10.1016/j.ijepes.2018.07.055
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper develops an optimal simultaneous hourly reconfiguration and day-ahead scheduling framework in smart distribution systems considering the operation of the protection devices. The objective function of the model is defined as the minimization of system's costs in terms of the costs associated with the purchased power from the wholesale market as well as the distributed generation (DG) owners, cost of switching actions, power losses cost and the cost for implementation of demand response (DR) programs. Moreover, a novel switching index along with the maximum number of switching actions based on the switch ages and critical branches in the network is presented. Due to the nonlinearity and non-convexity nature of the problem, the proposed optimization problem is then solved using a metaheuristic approach based on particle swarm optimization (PSO). As the result of the optimization process, the optimal set-points of DGs and responsive loads together with the optimal radial configuration of the distribution system for each hour of the scheduling time horizon are determined. To investigate the effect of DR programs and hourly reconfiguration on the load profile of the system, different price-based DR actions combined with interruptible load programs are also considered. Moreover, to demonstrate the satisfactory performance of the proposed model, the IEEE 33-bus distribution test system is thoroughly interrogated.
引用
收藏
页码:537 / 548
页数:12
相关论文
共 31 条
[1]   Distribution network reconfiguration using a genetic algorithm with varying population size [J].
Abdelaziz, Morad .
ELECTRIC POWER SYSTEMS RESEARCH, 2017, 142 :9-11
[2]   Incorporating short-term topological variations in optimal energy management of MGs considering ancillary services by electric vehicles [J].
Anand, M. P. ;
Golshannavaz, Sajjad ;
Ongsakul, Weerakorn ;
Rajapakse, Athula .
ENERGY, 2016, 112 :241-253
[3]   Cost-effective and comfort-aware residential energy management under different pricing schemes and weather conditions [J].
Anvari-Moghaddam, Amjad ;
Monsef, Hassan ;
Rahimi-Kian, Ashkan .
ENERGY AND BUILDINGS, 2015, 86 :782-793
[4]   Optimal use of incentive and price based demand response to reduce costs and price volatility [J].
Asadinejad, Ailin ;
Tomsovic, Kevin .
ELECTRIC POWER SYSTEMS RESEARCH, 2017, 144 :215-223
[5]   Reconfiguration of Smart Distribution Systems With Time Varying Loads Using Parallel Computing [J].
Asrari, Arash ;
Lotfifard, Saeed ;
Ansari, Meisam .
IEEE TRANSACTIONS ON SMART GRID, 2016, 7 (06) :2713-2723
[6]   Comprehensive Cost Minimization in Distribution Networks Using Segmented-Time Feeder Reconfiguration and Reactive Power Control of Distributed Generators [J].
Chen, Shuheng ;
Hu, Weihao ;
Chen, Zhe .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2016, 31 (02) :983-993
[7]   Optimal reconfiguration of distribution systems with representation of uncertainties through interval analysis [J].
de Oliveira, Leonardo W. ;
Seta, Felipe da S. ;
de Oliveira, Edimar J. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2016, 83 :382-391
[8]   Development of a thermal and electrical energy management in residential building micro-grid [J].
Esmaeili, S. ;
Vahidi, B. ;
Parvizimosaed, M. ;
Brahman, F. .
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2014, 6 (01)
[9]   Particle swarm optimization to solving the economic dispatch considering the generator constraints [J].
Gaing, ZL .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2003, 18 (03) :1187-1195
[10]   Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization [J].
Ghamisi, Pedram ;
Benediktsson, Jon Atli .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2015, 12 (02) :309-313