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Considering the importance of reducing system operating costs and controlling pollutant emissions by optimizing the operation of the integrated energy system, the energy supply structure of the integrated energy system and the joint multiobjective optimization dispatching structure is analyzed in this paper based on a day-ahead economic optimization dispatching model of the integrated energy system. Afterwards, the multiobjective optimization model of the integrated energy system is studied and multiobjective hierarchical progressive parallel algorithm based on improved NSGA-II is proposed according to the characteristics of the model. The algorithm improves the nondominated layer sorting algorithm, changes the convergence judgment condition while introducing the target reaching method to accelerate convergence, and introduces parallel computing technology according to the characteristics of the algorithm. The case shows that the proposed algorithm not only has advantages on the diversity in searching solutions but also can achieve better results in many aspects such as the iteration time and algorithm convergence which are required in practical engineering projects.

The high penetration of renewable energy brings great challenges to power system operation and scheduling. In this paper, a multi-timescale coordinated method for source-grid-load is proposed. First, the multi-timescale characteristics of wind forecasting power and demand response (DR) resources are described, and the coordinated framework of source-grid-load is presented under multi-timescale. Next, economic scheduling models of source-grid-load based on multi-timescale DR under network constraints are established in the process of day-ahead scheduling, intraday scheduling, and real-time scheduling. The loads are classified into three types in terms of different timescale. The security constraints of grid side and time-varying DR potential are considered. Three-stage stochastic programming is employed to schedule resources of source side and load side in day-ahead, intraday, and real-time markets. The simulations are performed in a modified Institute of Electrical and Electronics Engineers (IEEE) 24-node system, which shows a notable reduction in total cost of source-grid-load scheduling and an increase in wind accommodation, and their results are proposed and discussed against under merely two timescales, which demonstrates the superiority of the proposed multi-timescale models in terms of cost and demand response quantity reduction.

This study proposes a day-ahead optimal energy dispatch model for integrated energy system based on AC/DC interconnected infrastructure. In the infrastructure, micro-turbines turn high-frequency AC power into industrial-frequency AC power and incorporate it into the energy system's power system. Within the process, waste heat is sent into the waste heat boiler and lithium bromide chillier to interconnect with the heating and cooling systems. DC battery and photovoltaic generations connected with the energy system's power grid through the inverter are used as the flexible energy storage and power generation unit to build an autonomous integrated energy system based on AC/DC interconnection. In the optimisation model, the goal is set as system's lowest daily operation cost and lowest energy consumption. The constraints include system operation constraints and equipment constraints. Simulation results show that by performing the schedule made by the dispatch model, the operating efficiency of various types of AC/DC equipment in the energy system has been improved, energy waste scene has been reduced significantly and energy consumption in the energy system is reduced by 16% compared with other energy supply structure without AC/DC interconnected infrastructure under traditional schedule strategies.

With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.

A real-time error correction operation model for an integrated energy system is proposed in this paper, based on the analysis of the real-time optimized operation structure of an integrated energy system and the characteristics of the system. The model makes real-time corrections to the day-ahead operation strategy of the integrated energy system, to offset forecast errors from the renewable power generation system and multi-energy load system. When unbalanced power occurs in the system due to prediction errors, the model comprehensively considers the total capacity of each energy supply and energy storage equipment, adjustable margin, power climbing speed and adjustment cost, to formulate the droop rate which determines the unbalanced power that each device will undertake at the next time interval, while taking the day-ahead dispatching goals of the system into consideration. The case study shows that the dispatching strategy obtained by the real-time error correction operation model makes the power output change trend of the energy supply equipment consistent with the day-ahead dispatching plan at the next time interval, which ensures the safety, stability and economy of the real-time operation of the integrated energy system.

This paper proposes a day-ahead optimal economic dispatch model for building Combined Cooling, Heat and Power (CCHP) system based on centralized energy storage infrastructure. In the model, the loads are met by the centralized energy storage equipment directly, and the storage equipment are charged by varieties of energy supply devices in the energy station of the building CCHP system. The model is solved by the interior point method with Hessian matrix iteration. Simulation results show that by performing the schedule made by the dispatch model, the storage status of the centralized energy storage fluctuates in large dynamic range by flexible charging and discharging and the daily operation cost of the building CCHP system decreases remarkably compared with other traditional schedule strategies. DOI: http://dx.doi.org/10.5755/j01.eie.24.4.21478

The energy supply center of the multi energy interconnected region is an energy station, which contains many types of energy supply equipment to match the cold, heating and power loads. This paper proposed a day-ahead optimal economic dispatch model for multi energy interconnected region based on centralized and interconnected energy exchange framework. In the model, the constraints of regional network topology are taken into account. The model is solved by the interior point method in this paper. A case study shows that by performing the schedule made by the dispatch model, the daily operation cost of the multi energy interconnected region decreasing remarkably, thus demonstrates the effectiveness of the proposed economic dispatch schedule.

Storage devices are indispensable elements in a microgrid to compensate for the power imbalance between loads and the distributed generator (DG) output. Different storage strategies give diverse performances in adjustment speed and capacity. Based on the performance of different storage devices and the features of power imbalance curve in different periods, a classification control strategy is proposed in this paper. First, storage devices are given priorities according to the adjusting speed, and the power imbalance curve is divided into two periods according to the changing speed. During an emergency, all the storages are employed to compensate the rapidly increasing power imbalance; during the steady period, storage device with higher priority is employed to track the instantaneous component of power imbalance, while storage device with lower priority is used to replace the kWh output of higher priority storage. The proposed strategy is tested in a microgrid within the subdistribution network of IEEE RBTS Bus6 system using Monte Carlo methods. Simulation results indicate that the classification control strategy could endow the system with fast adjusting ability through coordination between storages. Reliability indices of the testing system are utilized to demonstrate that the classification control strategy could improve the stability of the microgrid and reliability of the distribution network. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.