Joint Planning of Active Distribution Network and EV Charging Stations Considering Vehicle-to-Grid Functionality and Reactive Power Support

This paper proposes a collaborative planning model for active distribution network(ADN)and electric vehicle(EV)charging stations that fully considers vehicle-to-grid(V2G)function and reactive power support of EVs in different regions.This paper employs a sequential decomposition method based on physical characteristics of the problem,breaking down the holistic problem into two sub-problems for solution.Subproblem I optimizes the charging and discharging behavior of autopilot electric vehicles(AEVs)using a mixed-integer linear programming(MILP)model.Subproblem II uses a mixed-integer secondorder cone programming(MISOCP)model to plan ADN and retrofit or construct V2G charging stations(V2GCS),as well as multiple distributed generation resources(DGRs).The paper also analyzes the impact of bi-directional active-reactive power interaction of V2GCS on ADN planning.The presented model is tested in the 47-node ADN in Longgang District,Shenzhen,China,and the IEEE 33-node ADN,demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.

Optimal Time-of-use Pricing for Renewable Energy-powered Microgrids: A Multi-agent Evolutionary Game Theory-based Approach

While price schedules can help improve the economic efficiency of renewable energy-powered microgrids,timeof-use(TOU)pricing has been identified as an effective way for microgrid development,which is presently limited by its high costs.In this study,we propose an evolutionary game theoretic model to explore optimal TOU pricing for development of renewable energy-powered microgrids by applying a multi-agent system,that comprises a government agent,local utility company agent,and different types of consumer agents.In the proposed model,we design objective functions for the company and the consumers and obtain a Nash equilibrium using backward induction.Two pricing strategies,namely,the TOU seasonal pricing and TOU monthly pricing,are evaluated and compared with traditional fixed pricing.The numerical results demonstrate that TOU schedules have significant potential for development of renewable energy-powered microgrids and are recommended for an electric company to replace traditional fixed pricing.Additionally,TOU monthly pricing is more suitable than TOU seasonal pricing for microgrid development.

Energy Internet:Redefinition and categories

The concept of‘Energy Internet’(EI)has been widely accepted by both academic and industry experts after more than a decade of development.Since it was proposed,EI has been discussed and applied to many technical works in power and energy areas.Some specific definitions were proposed for EI by those who have applied it to respective fields of engineering,but a comprehensive and widely accepted definition of EI is still being debated.In this paper,we propose the redefinition of EI,based on a comprehensive literature review,some latest trends and driving forces in the global energy industry,as well as its development in the past decade.In addition,we summarise the EI framework and features for future applications,where EI is categorised by its scale into local‐and wide‐area applications to manifest its effectiveness in power and energy.

Comprehensive power-supply planning for active distribution system considering cooling,heating and power load balance

An active distribution system power-supply planning model considering cooling,heating and power load balance is proposed in this paper.A regional energy service company is assumed to be in charge of the investment and operation for the system in the model.The expansion of substations,building up distributed combined cooling,heating and power(CCHP),gas heating boiler(GHB)and air conditioner(AC)are included as investment planning options.In terms of operation,the load scenarios are divided into heating,cooling and transition periods.Also,the extreme load scene is included to assure the power supply reliability of the system.Numerical results demonstrate the effectiveness of the proposed model and illustrate the economic benefits of applying distributed CCHP in regional power supply on investment and operation.

Primary frequency response based rescheduling for enhancing microgrid resilience

The existing microgrid operation schemes do not consider the dynamic performance of frequency in the islanded operation of microgrids.When an islanded microgrid encounters a disturbance,the sudden power mismatch could impose security risks or even a system collapse.To address such a challenge,this paper proposes the primary frequency response rescheduling(PFRR)approach.For a certain operation interval,the PFRR will optimally reschedule the distributed generators(DGs)with non-zero mechanical inertia and adjust the battery power.And the objective is to limit the rate-of-change-of-frequency(ROCOF)and to maintain the post-disturbance frequency nadir above a prescribed threshold.The effectiveness of the proposed strategy is verified by a case study on the IEEE 123-node test feeder system and the timedomain simulation in MATLAB Simulink.

Very Short-term Spatial and Temporal Wind Power Forecasting: A Deep Learning Approach

In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power dispatch.Conventional univariate wind power forecasting methods at presentonly utilize individual wind farm historical data.However,studies have shown that forecasting accuracy canbe improved by exploring both spatial and temporal correlations among adjacent wind farms.Current research on spatial-temporal wind power forecasting is based on relatively shallow time series models that,to date,have demonstrated unsatisfactory performance.In this paper,a convolution operation is used to capture the spatial and temporal correlations among multiple wind farms.A novel convolution-based spatial-temporal wind power predictor(CSTWPP)is developed.Due to CSTWPP’s high nonlinearity and deep architecture,wind power variation features and regularities included in the historical data can be more effectively extracted.Furthermore,the online training of CSTWPP enables incremental learning,which makes CSTWPP non-stationary and in conformity with real scenarios.Graphics processing units(GPU)is used to speed up the training process,validating the developed CSTWPP for real-time application.Case studies on 28 adjacent wind farms are conducted to show that the proposed model can achieve superior performance on 5-30 minutes ahead wind power forecasts.

Combined Electric and Heat System Testbeds for Power Flow Analysis and Economic Dispatch

In combined electric and heat systems,selecting a suitable testbed for power flow analysis or economic dispatch is not easy:a large number of existing testbeds are not opensource,while others are difficult to be reused by other researchers due to the particularity of system scale,topology,and data.In this paper,we present three open-source testbeds with different scales based on practical combined electric and heat systems.To satisfy researchers"specific demands on the system topology and data,we also discuss how to modify testbeds based on existing topologies and data.Researchers can use the testbeds presented in this paper to test their innovative methods for power flow analysis and economic dispatch,and can also design their own testbeds based on the methodology in this paper,using published topologies and data.

Application of Modified Progressive Hedging for Stochastic Unit Commitment in Electricity-Gas Coupled Systems

The increasing number of gas-fired units has significantly intensified the coupling between electric and gas power networks.Traditionally,nonlinearity and nonconvexity in gas flow equations,together with renewable-induced stochasticity,resulted in a computationally expensive model for unit commitment in electricity-gas coupled integrated energy systems(IES).To accelerate stochastic day-ahead scheduling,we applied and modified Progressive Hedging(PH),a heuristic approach that can be computed in parallel to yield scenario-independent unit commitment.Through early termination and enumeration techniques,the modified PH algorithm saves considerable com,putational time for certain generation cost settings or when the scale of the IES is large.Moreover,an adapted second-order cone relaxation(SOCR)is utilized to tackle the nonconvex gas flow equation.Case studies were performed on the IEEE 24.bus system/Belgium 20-node gas system and the IEEE 118-bus system/Belgium 20-node gas system.The computational efficiency when employing PH is 188 times that of commercial software,and the algorithm even outperforms Benders Decomposition.At the same time,the gap between the PH algorithm and the benchmark is less than 0.01% in both IES systems,which proves that the solutions produced by PH reach acceptable optimality in this stochastic UC problem.