Quick Hosting Capacity Evaluation Based on Distributed Dispatching for Smart Distribution Network Planning with Distributed Generation

The smart distribution network(SDN)is integrat ing increasing distributed generation(DG)and energy storage(ES).Hosting capacity evaluation is important for SDN plan ning with DG.DG and ES are usually invested by users or a third party,and they may form friendly microgrids(MGs)and operate independently.Traditional centralized dispatching meth od no longer suits for hosting capacity evaluation of SDN.A quick hosting capacity evaluation method based on distributed optimal dispatching is proposed.Firstly,a multi-objective DG hosting capacity evaluation model is established,and the host ing capacity for DG is determined by the optimal DG planning schemes.The steady-state security region method is applied to speed up the solving process of the DG hosting capacity evalua tion model.Then,the optimal dispatching models are estab lished for MG and SDN respectively to realize the operating simulation.Under the distributed dispatching strategy,the dual-side optimal operation of SDN-MGs can be realized by several iterations of power exchange requirement.Finally,an SDN with four MGs is conducted considering multiple flexible resources.It shows that the DG hosting capacity of SDN oversteps the sum of the maximum active power demand and the rated branch capacity.Besides,the annual DG electricity oversteps the maximum active power demand value.

Distributed Dispatch of Multiple Energy Systems Considering Carbon Trading

As an integrated carrier of energy production,transmission,distribution,conversion,storage,and utilization,multiple energy systems(MESs)have significant low-carbon potential.This paper proposes a hierarchical distributed dispatch model of MESs considering carbon trading,which is composed of the lower autonomous operation level of each MES and the upper coordinated control level.Different carbon emission sources are considered,including combined heat and power(CHP)units,gas boilers,and power to gas(P2G)devices.The transactive control(TC)mechanism is used to solve the model by introducing a virtual price signal.In the case study based on a 3-MES system,the effectiveness of the proposed distributed method is proved by comparison with a centralized algorithm.Meanwhile,the impacts of different carbon prices on MESs with different resource endowments are analyzed from the aspects of scheduling results,carbon emissions,clean energy consumption rate,and comprehensive operating costs.

Economic Dispatch of Electrical Power System Based on the Multi-objective Co-evolutionary Algorithm

It is important to distribute the load efficiently to minimize the cost of the economic dispatch of electrical power system. The uncertainty and volatility of wind energy make the economic dispatch much more complex when the general power systems are combined with wind farms. The short term wind power prediction method was discussed in this paper. The method was based on the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD). Furthermore,the effect of wind farms on the traditional economic dispatch of electrical power system was analyzed. The mathematical model of the economic dispatch was established considering the environmental factors and extra spinning reserve cost. The multi-objective co-evolutionary algorithm was used to figure out the model. And the results were compared with the NSGA-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ) to verify its feasibility.

Recent progress on the study of distributed economic dispatch in smart grid:an overview

Designing an efficient distributed economic dispatch(DED)strategy for the smart grid(SG)in the presence of multiple generators plays a paramount role in obtaining various benefits of a new generation power syst em,such as easy implementation,low maintenance cos t,high energy efficiency,and strong robus tn ess agains t uncertainties.It has drawn a lot of interest from a wide variety of scientific disciplines,including power engineering,control theory,and applied mathematics.We present a state-of-the-art review of some theoretical advances toward DED in the SG,with a focus on the literature published since 2015.We systematically review the recent results on this topic and subsequently categorize them into distributed discrete-and continuous-time economic dispatches of the SG in the presence of multiple generators.After reviewing the literature,we briefly present some future research directions in DED for the SG,including the distributed security economic dispatch of the SG,distributed fast economic dispatch in the SG with practical constraints,efficient initialization-free DED in the SG,DED in the SG in the presence of smart energy storage batteries and flexible loads,and DED in the SG with artificial intelligence technologies.

Fully Distributed Economic Dispatch for Cyber-physical Power System with Time Delays and Channel Noises

Economic dispatch problem(EDP)is a fundamental optimization problem in power system operation,which aims at minimizing the total generation cost.In fact,the power grid is becoming a cyber-physical power system(CPPS).Therefore,the quality of communication is a key point.In this paper,considering two important factors,i.e.,time delays and channel noises,a fully distributed consensus based algorithm is proposed for solving EDP.The critical maximum allowable upper bounds of heterogeneous communication delays and self-delays are obtained.It should be pointed out that the proposed algorithm can be robust against the time-varying delays and channel noises considering generator constraints.In addition,even with time-varying delays and channel noises,the power balance of supply and demand is not broken during the optimization.Several simulation studies are presented to validate the correctness and superiority of the developed results.

Short-term Scheduling of Steam Power System in Iron and Steel Industry under Time-of-use Power Price

A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cost,equipment maintenance cost and the charge of exchange power with main grid.The model took into account the varying nature of surplus byproduct gas flows,several practical technical constraints and the impact of TOU power price.All major types of utility equipments,involving boilers,steam turbines,combined heat and power（CHP）units,and waste heat and energy recovery generators（WHERG）,were separately modeled using thermodynamic balance equations and regression method.In order to solve this complex nonlinear optimization model,a new improved particle swarm optimization（IPSO）algorithm was proposed by incorporating time-variant parameters,a selfadaptive mutation scheme and efficient constraint handling strategies.Finally,a case study for a real industrial example was used for illustrating the model and validating the effectiveness of the proposed approach.