Fully privacy-preserving distributed optimization in power systems based on secret sharing

With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems.Traditionally,data exchange among parties is inevitable,rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue.This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation(SMPC)with secret sharing protocols.The framework decomposes the collaborative optimization problem into a master problem and several subproblems.The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents.The relationships of agents are completely equal,and there is no privileged agent or any third party.The process of solving subproblems is conducted by agents individually.Compared to the traditional distributed optimization framework,the proposed SMPC-based framework can fully preserve individual private information.Exchanged data among agents are encrypted and no private information disclosure is assured.Furthermore,the framework maintains a limited and acceptable increase in computational costs while guaranteeing opti-mality.Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.

Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment

Data-driven artificial intelligence technologies have emerged as increasingly fascinating tools for assessing power system security.However,their inherent mechanism of inexplicability and unreliability now limits their scalability in power systems.To address this problem,this paper proposes a neural network design method empowered by physical mechanisms for power system security assessment.It incorporates geometric characteristics of dynamic security regions into the network training process and constructs connections between network structure and system's unstable mode,which can perform security assessment with a neural network efficiently while ensuring physical plausibility.Furthermore,a credibility evaluation mechanism is established to ensure credibility of neural network predictions and reduce misclassifications.Finally,effectiveness of the proposed method is verified on test systems.Methods and considerations in building a neural network with interpretable structures and credible predictions can provide a reference for machine intelligence applied in other industrial systems.

Energy Management of Price-maker Community Energy Storage by Stochastic Dynamic Programming

In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy storage.As a price-maker,energy storage smooths price differences,thus decreasing energy arbitrage value.However,this price-smoothing effect can result in significant external welfare changes by reduc-ing consumer costs and producer revenues,which is not negligible for the community with energy storage systems.As such,we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare.To incorporate market interaction into the SDP format,we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices.Then we present an analytical SDP algorithm that does not require state discretization.Apart from computational efficiency,another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value.Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage.The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.Index Terms-Analytical stochastic dynamic programming,energy management,energy storage,price-maker,social welfare.

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.

Quasi-dynamic Interactions and Security Control of Integrated Electricity and Heating Systems in Normal Operations

Coupling between electricity systems and heating systems are becoming stronger,leading to more flexible and more complex interactions between these systems.The operation of integrated energy systems is greatly affected,especially when security is concerned.Steady-state analysis methods have been widely studied in recent research,which is far from enough when the slow thermal dynamics of heating networks are introduced.Therefore,an integrated quasi-dynamic model of integrated electricity and heating systems is developed.The model combines a heating network dynamic thermal model and the sequential steady-state models of electricity networks,coupling components,and heating network hydraulics.Based on this model,a simulation method is proposed and quasi-dynamic interactions between electricity systems and heating systems are quantified with the highlights of transport delay.Then the quasi-dynamic interactions were applied using security control to relieve congestion in electricity systems.Results show that both the transport delay and control strategies have significant influences on the quasi-dynamic interactions.

Active Dynamic Aggregation Model for Distributed Integrated Energy System as Virtual Power Plant

Distributed integrated multi-energy systems(DIMSs)can be regarded as virtual power plants to provide additional flexibility to the power system.This paper proposes a robust active dynamic aggregation model for the DIMSs to describe the maximum feasible region.The aggregation model includes the power constraints,energy constraints,and ramping constraints to aggregate different types of resources in the DIMSs.The proposed generator-like and storage-like model does not depend on the ancillary service market and can be directly incorporated into the economic dispatch model of the power system.A novel algorithm based on the column-and-constraint generation algorithm and convex-concave procedure is proposed to solve the two-stage robust optimization problem,which is more efficient than the KKT-based algorithms.Finally,a case study of an actual DIMS is developed to demonstrate the effectiveness of the proposed model.

A Hybrid State Estimation Approach for Integrated Heat and Electricity Networks Considering Time-scale Characteristics

State estimation(SE)usually serves as the basic function of the energy management system(EMS).In this paper,the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established.Then,a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline.Meanwhile,to accommodate the measuring resolutions of the integrated network,a hybrid SE approach is developed based on the two-stage iterative algorithm.Results show that,in both steady and dynamic processes,the two-stage estimator has good accuracy and convergence.The hybrid estimator has good performance on tracking the variation of the states in the heating network,even when the available measurements are limited.

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.

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.

Security Analysis of a Park-level Agricultural Energy Network Considering Agrometeorology and Energy Meteorology

China’s industrial manufacturing industry is well developed,but its agriculture is primitive.The only way to solve this problem is to improve through modern agriculture.The cross integration of new energy development and modern agriculture is becoming more and more critical.However,the research on the interaction between the meteorological disaster of facility agriculture and the power supply security of the integrated energy supply system has not formed a systematic theoretical system,which challenges the collaborative security of the facility agriculture and energy system.In this paper,energy meteorology and agrometeorology are considered and modeled,and the static security of a park-level agricultural energy network is simulated and analyzed under different weather conditions.

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.

Data-driven Transient Stability Assessment Using Sparse PMU Sampling and Online Self-check Function

Artificial intelligence technologies provide a newapproach for the real-time transient stability assessment (TSA)of large-scale power systems. In this paper, we propose a datadriven transient stability assessment model (DTSA) that combinesdifferent AI algorithms. A pre-AI based on the time-delay neuralnetwork is designed to locate the dominant buses for installingthe phase measurement units (PMUs) and reducing the datadimension. A post-AI is designed based on the bidirectionallong-short-term memory network to generate an accurate TSAwith sparse PUM sampling. An online self-check function of theonline TSA’s validity when the power system changes is furtheradded by comparing the results of the pre-AI and the post-AI.The IEEE 39-bus system and the 300-bus AC/DC hybrid systemestablished by referring to China’s existing power system areadopted to verify the proposed method. Results indicate that theproposed method can effectively reduce the computation costswith ensured TSA accuracy as well as provide feedback forits applicability. The DTSA provides new insights for properlyintegrating varied AI algorithms to solve practical problems inmodern power systems.

A two-level hierarchical discrete-device control method for power networks with integrated wind farms

Power systems depend on discrete devices, such as shunt capacitors/reactors and on-load tap changers, for their long-term reliability.In transmission systems that contain large wind farms, we must take into account the uncertainties in wind power generation when deciding when to operate these devices.In this paper, we describe a method to schedule the operation of these devices over the course of the following day.These schedules are designed to minimize wind-power generation curtailment, bus voltage violations, and dynamic reactive-power deviations,even under the worst possible conditions.Daily voltagecontrol decisions are initiated every 15 min using a dynamic optimization algorithm that predicts the state of the system over the next 4-hour period.For this, forecasts updated in real-time are employed, because they are more precise than forecasts for the day ahead.Day-ahead schedules are calculated using a two-stage robust mixedinteger optimization algorithm.The proposed control strategies were tested on a Chinese power network with wind power sources; the control performance was also validated numerically.

Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints

Preventive transient stability control is an effective measure for the power system to withstand high-probability severe contingencies.It is mathematically an optimal power flow problem with transient stability constraints.Due to the constraints involved for differential algebraic equations of transient stability,it is difficult and time-consuming to solve this problem.To address these issues,this paper presents a novel deep reinforcement learning(DRL)framework for preventive transient stability control of power systems.A distributed deep deterministic policy gradient is utilized to train a DRL agent that can learn its control policy through massive interactions with a grid simulator.Once properly trained,the DRL agent can instantaneously provide effective strategies to adjust the system to a safe operating position with a near-optimal operational cost.The effectiveness of the proposed method is verified through numerical experiments conducted on a New England 39-bus system and NPCC 140-bus system.

A Potential Security Threat and Its Solution in Coupled Urban Power-traffic Networks with High Penetration of Electric Vehicles

The growing penetration of electric vehicles(EVs)and the popularity of fast charging stations(FCSs)have greatly strengthened the coupling of the urban power network(PN)and traffic network(TN).In this paper,a potential security threat of the PN-TN coupling is revealed.Different from traditional loads,a regional FCS outage can lead to both the spatial and temporal redistribution of EV charging loads due to EV mobility,which further leads to a power flow redistribution.To assess the resulting potential threats,an integrated PN-TN modeling framework is developed,where the PN is described by a direct current optimal power flow model,and the TN is depicted by an energy-constraint traffic assignment problem.To protect the privacy of the two networks,an FCS outage distribution factor is proposed to describe the spatial-temporal redistribution ratio of the charging load among the remaining I FCSs.Moreover,to protect the security of the coupled networks,a price-based preventive regulation method,based on the spatial demand elasticity of the EV charging load,is developed to reallocate the charging load as a solution for insecure situations.Numerical simulation results validate the existence of the PN-TN coupling threat and demonstrate the effectiveness of the regulation method to exploit the spatial flexibility of EV loads.

Impacts of Optimization Interval on Home Energy Scheduling for Thermostatically Controlled Appliances

Home energy management systems(HEMS)have attracted much attention in recent years for energy efficiency and cost savings.Home energy scheduling is an important function of the HEMS,especially for thermostatically controlled appliances(TCAs).Optimization interval is a basic parameter in home energy scheduling,a topic that has been seldom studied before.This paper studies the impacts of optimization interval on home energy scheduling taking into consideration four scheduling strategies for TCAs.A tracking strategy is developed to arrive at a suboptimal solution while avoiding unacceptable solving time.The impact mechanism of optimization interval is analyzed.The optimization interval takes into account the scheduling ability of HEMS,flexibility of TCAs,the feasibility of scheduling,scheduling performances,user experiences,and model accuracy.The flexibility of TCAs,which depends on optimization interval,is defined and modeled.Two time division methods are proposed,namely,consistent interval division method(CIDM)and inconsistent interval division method(IIDM).Numerical simulation is carried out to verify the analysis.The results show that optimization interval impacts the flexibility,feasibility,and performance of scheduling.The proposed tracking strategy is seen as an effective method for HEMS.

Extended Sufficient Conditions for Exact Relaxation of the Complementarity Constraints in Storage-concerned Economic Dispatch

Storage is widely considered in economic dispatch(ED)problems.To prevent simultaneous charging and discharging of a storage device,a storage-concerned ED problem should involve complementarity constraints for every storage device to make the problem strongly non-convex.In this case,the conventional Karush-Kuhn-Tucker optimality conditions are unsuitable,and the methods that are normally effective are also invalid.In our recent paper,we proposed a new exact relaxation method that directly removes the complementarity constraints from a storageconcerned ED model to make it convex and easy to solve.This paper extends the previous study by presenting and analyzing two new groups of sufficient conditions that guarantee exact relaxation.Different application conditions of these groups of sufficient conditions are discussed.Numerical tests are performed to show the benefit of using the exact relaxation method and the different suitable application conditions of these groups of sufficient conditions.This paper contributes to a wide application of exact relaxation in storage-concerned ED problems.

Cyber-physical Test Case for Distribution Grid Operation and Control

In the CPS-oriented power distribution system,a large number of the existing test cases cannot be accessed and reused.That is not conducive to the continuity of the CPS research of the distribution network.In response to above problem,based on an actual distribution network and considering the mapping relationship between cyber systems and physical systems,a computation test case that covers multiple power sources,and multiple types of load is proposed in this paper,and it is suitable for the simulation of multiple types of information system scenarios.In order to satisfy the specific needs of researchers for system topology and data,how to perform cyber contingency analysis,vulnerability assessment and distributed control are also discussed based on the existing topology and data.Researchers can utilize the test case presented in this paper to test their innovative methods in operational analysis,optimization control,and safety analysis for distribution networks.They can also utilize the published topologies and data to design their own test cases based on the methods in this paper.

Online Frequency Security Assessment Based on Analytical Model Considering Limiting Modules

Due to the increasing implementation of high voltage direct current(HVDC)and the integration of renewable resources,frequency stability problems in power systems are drawing greater attention in recent years.It has become necessary to carry out online frequency security assessments to ensure the safe operation of power systems.Considering the low time-efficiency of simulation-based methods,analytical models,such as the frequency nadir prediction(FNP)model,are more suitable for online assessment,which requires calculating the worst frequency deviation under various contingencies.Based on the FNP model,the FNP-L model for online frequency security assessment is proposed in this paper.The proposed model implements security assessment by calculating and checking the frequency features,including the nadir time and frequency,followed by contingencies.The effect of the governor,including nonlinear constraints,is approximated into polynomial functions so that the results are obtained by solving multiple polynomial equations.Case studies are carried out using the New-England 39-bus system and a regional power grid,which shows that the proposed model could achieve both high speed and high accuracy,and can therefore be applied in online security assessment.

Novel Planning Approach for Fast-charging Station in Integrated System

A promising way to boost popularity of electric vehicles(EVs)is to properly layout fast charging stations(FCSs)by jointly considering interactions among EV drivers,power systems and traffic network constraints.This paper proposes a novel sensitivity analysis-based FCS planning approach,which considers the voltage sensitivity of each sub-network in the distribution network and charging service availability for EV drivers in the transportation network.In addition,energy storage systems are optimally installed to provide voltage regulation service and enhance charging capacity.Simulation tests conducted on two distribution network and transportation network coupled systems validate the efficacy of the proposed approach.Moreover,comparison studies demonstrate the proposed approach outperforms a Voronoi graph and particle swarm optimization combined planning approach in terms of much higher computation efficiency.