Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Dynamic Balance Control Based on an Adaptive Gain-scheduled Backstepping Scheme for Power-line Inspection Robots

This paper presents an adaptive gain-scheduled backstepping control(AGSBC) scheme for the balance control of an underactuated mechanical power-line inspection(PLI) robotic system with two degrees of freedom and a single control input.First, a nonlinear dynamic model of the balance adjustment process of the PLI robot is constructed, and then the model is linearized at a nominal equilibrium point to overcome the computational infeasibility of the conventional backstepping technique. Second, to solve generalized stabilization control issue for underactuated systems with multiple equilibrium points,an equilibrium manifold linearized model is developed using a scheduling variable, and then a gain-scheduled backstepping control(GSBC) scheme for expanding the operational area of the controlled system is constructed. Finally, an adaptive mechanism is proposed to counteract the impact of external disturbances. The robust stability of the closed-loop system is ensured by Lyapunov theorem. Simulation results demonstrate the effectiveness and high performance of the proposed scheme compared with other control schemes.

Power flow calculation for a distribution system with multi-port PETs:an improved AC-DC decoupling iterative method

Recently,power electronic transformers(PETs)have received widespread attention owing to their flexible networking,diverse operating modes,and abundant control objects.In this study,we established a steady-state model of PETs and applied it to the power flow calculation of AC-DC hybrid systems with PETs,considering the topology,power balance,loss,and control characteristics of multi-port PETs.To address new problems caused by the introduction of the PET port and control equations to the power flow calculation,this study proposes an iterative method of AC-DC mixed power flow decoupling based on step optimization,which can achieve AC-DC decoupling and effectively improve convergence.The results show that the proposed algorithm improves the iterative method and overcomes the overcorrection and initial value sensitivity problems of conventional iterative algorithms.

The Interaction between the Large-Scale EVs and the Power Grid

With the problem of global energy shortage and people’s awareness of energy saving, electric vehicles receive world-wide attention from government to business. Then the load of the power grid will rapidly increase in a short term, and a series of effects will bring to the power grid operation, management, production and planning. With the large-scale penetration of electric vehicles and distributed energy gradually increased, if they can be effectively controlled and regulated, they can play the roles of load shifting, stabling intermittent renewable energy sources, providing emergency power supply and so on. Otherwise they may have a negative impact, which calls for a good interaction of electric vehicles and power grid. Analyzed the status of the current study on the interaction between the electric vehicles and the power grid, this paper builds the material basis, information architecture and the corresponding control method for the interaction from the aspect of the energy and information exchanging, and then discusses the key issues, which makes a useful exploration for the further research.

A Method for the Design of UFLS Scheme with Dynamic Correction

This paper presents an adaptive Under Frequency Load Shedding scheme based on Wide Area Measurement System. Due to the lack of enough adaptability to the operation state of the system, the traditional successive approximation under frequency load shedding method will cause excessive cut or undercut problems inevitably. This method consists first in a comprehensive weight index including load characteristics and inertias of generators. Then active-power deficit calculation based on the Low-order Frequency Response Model, concerning the effect of voltage was put forward. Finally, a dynamic correction of the load shedding amount was proposed to modify the scheme. This approach was applied to IEEE-39 system and the simulation results indicated that the proposed method was effective in reducing the load shedding amount as well as the frequency recovery time.

A Scenario-classification Hybrid-based Banding Method for Power Transfer Limits of Critical Inter-corridors

To secure power system operations,practical dispatches in industries place a steady power transfer limit on critical inter-corridors,rather than high-dimensional and strong nonlinear stability constraints.However,computational complexities lead to over-conservative pre-settings of transfer limit,which further induce undesirable and non-technical congestion of power transfer.To conquer this barrier,a scenario-classification hybrid-based banding method is proposed.A cluster technique is adopted to separate similarities from historical and generated operating condition dataset.With a practical rule,transfer limits are approximated for each operating cluster.Then,toward an interpretable online transfer limit decision,costsensitive learning is applied to identify cluster affiliation to assign a transfer limit for a given operation.In this stage,critical variables that affect the transfer limit are also picked out via mean impact value.This enables us to construct low-complexity and dispatcher-friendly rules for fast determination of transfer limit.The numerical case studies on the IEEE 39-bus system and a real-world regional power system in China illustrate the effectiveness and conservativeness of the proposed method.

Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization

In the competitive energy market,energy retailers are facing the uncertainties of both energy price and demand,which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market.Particularly,the attractive multi-energy retail packages are the key for retailers to increase their benefit.Therefore,combined with incentive means and price signals,five types of multi-energy retail packages such as peak-valley time-of-use(TOU)price package and day-night bundled price package are designed in this paper for retailers.The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower(MLMF)Stackelberg game,in which retailers are leaders and end-users are followers.Retailers make decisions to maximize the profit considering the conditional value at risk(CVaR)while end-users optimize the satisfaction of both energy comfort and economy.Besides,a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization(PSO)algorithm and CPLEX solver are applied to solve the optimization model for each participant(retailer or end-user).Numeral results show that the designed retail packages can increase the overall profit of retailers,and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction.

Bounded Rationality Based Multi-VPP Trading in Local Energy Markets:A Dynamic Game Approach with Different Trading Targets

It is expected that multiple virtual power plants(multi-VPPs)will join and participate in the future local energy market(LEM).The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator(LEMO)and each VPP.We propose a bounded rationality-based trading model of multiVPPs in the local energy market by using a dynamic game approach with different trading targets.Three types of power bidding models for VPPs are first set up with different trading targets.In the dynamic game process,VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents’bidding strategies and its own clustered resources.LEMO would decide the electricity buying/selling price in the LEM.Furthermore,the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization(IPSO)algorithm and conventional largescale optimization.Finally,case studies are conducted to show the performance of the proposed model and solution approach,which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.

Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach

To utilize electricity in a clean and integrated manner,a zero-carbon hydro-photovoltaic(PV)-pumped hydro storage(PHS)integrated power system is studied,considering the uncertainties of PV and load demand.It is a challenge for operators to develop a dynamic dispatch mechanism for such a system,and traditional dispatch methods are difficult to adapt to random changes in the actual environment.Therefore,this study proposes a real-time dynamic dispatch strategy considering economic operation and complementary regulatory ability.First,the dynamic dispatch of a hydro-PV-PHS integrated power system is presented as a multi-objective optimization problem and the weight factor between different goals is effectively calculated using information entropy.Afterwards,the dispatch model is converted into the Markov decision process,where the dynamic dispatch decision is formulated as a reinforcement learning framework.Then,a deep deterministic policy gradient(DDPG)is deployed towards the online decision for dispatch in continuous action spaces.Finally,a case study is applied to evaluate the performance of the proposed method based on a real hydroPV-PHS integrated power system in China.Simulations show that the system agent reduces the power volatility of supply by 26.7%after hydropower regulating and further relieves power fluctuation at the point of common coupling(PCC)to the upperlevel grid by 3.28%after PHS participation.The comparison results verify the effectiveness of the proposed method.

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.

Formulation of Locational Marginal Electricity-carbon Price in Power Systems

Decarbonisation of power systems is essential for realising carbon neutrality,in which the economic cost caused by carbon needs to be qualified.Based on the formulation of locational marginal price(LMP),this paper proposes a locational marginal electricity-carbon price(EC-LMP)model to reveal carbon-related costs caused by power consumers.A carbon-priceintegrated optimal power flow(C-OPF)is then developed to maximise economic efficiency of the power system considering the costs of electricity and carbon.Case studies are presented to demonstrate the new formulation and results demonstrate the efficacy of the EC-LMP-based C-OPF on decarbonisation and economy.

负激励和延期支付对绩效的影响——基于一项实验

薪酬合同设计合理与否,不但关系到企业的整体财务绩效,而且关系到每个职工的切身利益。本文通过实验研究,将负激励和延期支付机制引入原有薪酬计划,观察其对绩效的影响。本文发现,在一般线性预算薪酬合同中加入负激励或延期支付机制,能够切实提升绩效并降低单位人工成本。本文运用实验研究的方法,基于心理学理论和管理会计学的视角,探讨不同的薪酬合同对个人任务绩效的激励效果,为负激励和延期支付的作用效果提供直接的经验证据,为企业如何在薪酬激励中运用负激励和延期支付提供理论指导。

Review and prospect of active distribution system planning

The approach to planning,design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources(DERs)together with load growth,energy storage technology advancements and increased consumer expectations.Planning of active distribution systems(ADS)has been a very hot topic in the 21st Century.A large number of studies have been done on ADS planning.This paper reviews the state of the art of current ADS planning.Firstly,the influences of DERs on the ADS planning are addressed.Secondly,the characteristics and objectives of ADS planning are summarized.Then,up to date planning model and some related research are highlighted in different areas such as forecasting load and distributed generation,mathematical model of ADS planning and solution algorithms.Finally,the paper explores some directions of future research on ADS planning including planning collaboratively with all elements combined in ADS,taking into account of joint planning in secondary system,coordinating goals among different layers,integrating detailed operation simulations and regular performance based reviews into planning,and developing advanced planning tools.

Incentive-based demand response model for maximizing benefits of electricity retailers

The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers.To address this issue,an incentive-based demand response(DR)model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers.The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions.The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price.Then,the sensitivity of incentive price on retailers’benefits is analyzed and the optimal incentive price is obtained according to the DR model.The case study verifies the effectiveness of the proposed model.

Optimal Planning of AC-DC Hybrid Transmission and Distributed Energy Resource System: Review and Prospects

With the highly-extensive integration of distributed renewable energy resources(DER)into the grid,the power distribution system has changed greatly in the structure,function and operating characteristics.On this ground,An AC-DC hybrid DER system becomes necessary for effective management and control over DER.This paper first summarizes the physical characteristics and morphological evolution of AC-DC hybrid DER system.The impact of these new features on system configuration planning is analyzed with respect to its flexible networking,rich operation control modes,and tight sourcenetwork-load-storage coupling.Then,based on a review of the existing research,problems and technical difficulties are figured out in terms of converter modeling,steady-state analysis,power flow calculation,operating scenarios management,and optimization model solution.In light of the problems and difficulties,a framework for the configuration optimization of AC-DC hybrid DER systems is proposed.At last,the paper provides a prospect of key technologies from six aspects including morphology forecasting,coupling interaction analysis,uncertainty modeling,operation simulation,optimization model solving algorithm and comprehensive scheme evaluation.

Clean Energy Consumption of Power Systems Towards Smart Agriculture: Roadmap, Bottlenecks and Technologies

Over the past decades,both agriculture and power systems have faced serious problems,such as the power supply shortage in agriculture,and difficulties of clean energy consump-tion in the power system.To address and overcome these issues,this paper proposes an idea to combine smart agriculture and clean energy consumption,use surplus clean energy to supply agriculture production,and utilize smart agriculture to support power system with clean energy penetration.A comprehensive review has been conducted to first depict the roadmap of coupling a agriculture-clean energy system,analyze their feasibilities and advantages.The recent technologies and bottlenecks are summa-rized and evaluated for the development of a combined system consisting of smart agriculture production and clean energy consumption.Several case studies are introduced to explore the mutual benefits of agriculture-clean energy systems in both the energy and food industries.

Active energy management strategies for active distribution system

Active energy management is an effective way to realize the flexible utilization of distributed energy resources to suit the characteristics of active distribution system.Advanced active energy management strategies need to be designed to coordinate the optimization of‘generation,network,load’.An active management model is built for the local distribution system integrated with the generation curtailment mechanism and the charging/discharging management of plug-in electric vehicles.Furthermore,different strategies based on the energy management model are presented.The model and strategies are tested and discussed in a modified distribution system,and the impacts with different load profiles are also analyzed.

Multi-stage sizing approach for development of utility-scale BESS considering dynamic growth of distributed photovoltaic connection

The battery energy storage system(BESS)is regarded as one of the most promising address operational challenges caused by distributed generations.This paper proposes a novel multi-stage sizing model for utility-scale BESS,to optimize the BESS development strategies for distribution networks with increasing penetration levels and growth patterns of dispersed photovoltaic(PV)panels.Particularly,an integrated model is established in order to accommodate dispersed PVs in short-term operation scale while facilitating appropriate profits in long-term planning scale.Clusterwise reduction is adopted to extract the most representative operating scenarios with PVs and BESS integration,which is able to decrease the computing complexity caused by scenario redundancy.The numerical studies on IEEE 69-bus distribution system verify the feasibility of the proposed multi-stage sizing approach for the utility-scale BESS.

A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

Demand response, the reactive power output of distributed generation(DG), and network reconfiguration have significant impacts on a DG allocation strategy. In this context, a novel real-time price-based demand response formulation is integrated into the allocation model of DG. The tariff is regulated by the difference between the load and active power of renewable energy. Meanwhile, network reconfiguration and the capacity curve describing the active and reactive power limits of DG are included in the optimization model for promoting the allocation of DG.With these measures, the optimal allocation model of DG is established with the goal of maximizing the net annual profit while guaranteeing the efficient utilization of renewable energy. In addition, the uncertainties of renewable energy are considered on the basis of a two-stage robust optimization method. Finally, the entire optimization model is solved by the column and constraint generation algorithm in the IEEE 33-bus distribution system and a practical 99-bus distribution system. Numerical simulations show that the proposed model is effective in terms of improving both the usage of renewable energy and net annual profit.

A new method of enhancing reliability for transmission expansion planning

The reliability plays a significant role in power systems and it is an important objective or constraint in transmission expansion planning.Firstly,a DC optimization model was proposed to calculate the maximum arrival power at each load point.Compared to the network flow method,DC model is closer to the actual power flow and it is able to obtain more realistic reliability assessment results.Furthermore,a novel sensitivity index(SI)was also proposed to choose the most effective line so as to enhance the nodal and/or system reliability.The Monte Carlo simulation is used to simulate the system components state.This improved reliability evaluation method and SI can be used for transmission expansion planning or maintenance scheduling.Tests are performed using 6-bus system derived from the Garver’s system and the IEEE 10-machine 39-bus system.The results show the effectiveness of the method.