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.

Transient of power pulse and its sequence in power electronics

Various failures and destructions occur in the applications of the power electronic converter. The real practice shows that these failures are connected with the con-centration of the transient power pulse. In allusion to the physical characteristics of power electronic converters,this paper proposed that the power pulse and its se-quence are the basis for power electronics in the perspective of electromagnetic energy. The authors analyzed the transient processes in the power semiconductors,electric conduction loops and controller system and illustrated the power pulse phenomena in high voltage and high power inverters. This investigation on the power pulse sequence is very meaningful for the failure analysis and device pro-tection and has become an important topic in power electronics.

Allocating Ex-post Deviation Cost of Virtual Power Plants in Distribution Networks

Virtual power plants(VPPs)including distributed generation,energy storage,and elastic load are emerging in distribution networks.Multiple VPPs can participate in electricity market as an aggregated entity and effective cost allocation mechanism among VPPs is a crucial issue.This paper focuses on allocating ex-post cost of VPPs incurred by deviation between actual power and ex-ante schedule in a two-settlement electricity market.We obtain approximate quadratic formulation of ex-post deviation cost considering network loss and develop an analytical cost allocation algorithm based on cooperative game theory.The allocated cost is consistent with cost causation principle and provides VPPs with incentive for aggregation.The proposed allocation method and relevant theoretical result are evaluated and verified by numerical tests.

Energy Storage in High Variable Renewable Energy Penetration Power Systems:Technologies and Applications

Integrating variable renewable energy is one of the most effective ways to achieve a low-carbon energy system.The high penetration of variable renewable energy,such as wind power and photovoltaic,increases the challenge of balancing the power system.Energy storage technology is regarded as one of the key technologies for balancing the intermittency of variable renewable energy to achieve high penetration.This study reviews the energy storage technology that can accommodate the high penetration of variable renewable energy.The basic energy storage technologies that can accommodate time-scale variation are reviewed first.The role of energy storage in the generation,transmission,distribution,and consumption for the high variable renewable energy penetration system is then analyzed.The supporting energy storage policies in the United States,the United Kingdom and China are summarized.Specific suggestions are proposed from the perspectives of technology,business and policy.This paper provides guidelines for planning energy storage to enable a high variable renewable energy penetration power system.

Optimal Aggregation Approach for Virtual Power Plant Considering Network Reconfiguration

As an aggregator of distributed energy resources(DERs) such as distributed generator, energy storage, and load,the virtual power plant(VPP) enables these small DERs participating in system operation. One of the critical issues is how to aggregate DERs to form VPPs appropriately. To improve the controllability and reduce the operation cost of VPP, the complementary DERs with close electrical distances should be aggregated in the same VPP. In this paper, it is formulated as an optimal network partition model for minimizing the voltage deviation inside VPPs and the fluctuation of injection power at the point of common coupling(PCC). A new convex formulation of network reconfiguration strategy is incorporated in this approach which can guarantee the components of the same VPP connected and further improve the performance of VPPs.The proposed approach is cast as an instance of mixed-integer linear programming(MILP) and can be effectively solved.Moreover, a scenario reduction method is developed to reduce the computation burden based on the k-shape algorithm. Numerical tests on the 13-bus and 70-bus distribution networks justify the effectiveness of the proposed approach.

Locational Pricing of Uncertainty Based on Robust Optimization

With the increasing penetration of renewables,power systems have to operate with greater flexibility to address the uncertainties of renewable output.This paper develops an uncertainty locational marginal price(ULMP)mechanism to price these uncertainties.They are denoted as box deviation intervals as suggested by the market participants.The ULMP model solves a robust optimal power flow(OPF)problem to clear market bids,aiming to minimize the system cost as a prerequisite that the reserve margin can address all the relevant uncertainties.The ULMP can be obtained as a by-product of the optimization problem from the Lagrange multipliers.Under the ULMP mechanism,renewables and consumers with uncertainty will make extra payments,and the thermals and financial transmission right(FTR)holders will be compensated.It is further shown that the proposed mechanism has preferable properties,such as social efficiency,budget balance and individual rationality.Numerical tests are conducted on the modified IEEE 5-bus and 118-bus systems to demonstrate the merits and applicability of the proposed mechanism.

Learning-based Data Analytics:Moving Towards Transparent Power Grids

this paper,we present the learning-based data analytics moving towards transparent power grids and provide some possible extensions including machine learning,big data analytics,and knowledge transferring.The closed loops of data and knowledge are illustrated and the challenges for establishing the closed loops are discussed.General ideas and recent developments in supervised learning,unsupervised learning,and reinforcement learning are presented together with extensions for power system applications.Furthermore,much emphasis is placed on privacypreserving data analysis,transfer of knowledge,machine learning for causal inference,scalability and flexibility of data analytics,and efficiency and reliability of computation.Existing integrated solutions in the industry featuring the Industrial Internet and the digital grid are also introduced.

Modeling Arbitrage of an Energy Storage Unit Without Binary Variables

Energy storage device cannot be operated in charging and discharging modes simultaneously.Existing model utilizes binary variables to enforce such a request of complementarity.This paper discusses the implementation of a non-complementary strategy and reveals that strict complementarity can be replaced with a weaker yet linear constraint without jeopardizing practical viability.Hence,the arbitrage of an energy storage unit can be modeled via a linear program.The proposed model provides additional dimension of flexibility which can improve the profit of electricity arbitrage.The linearity benefits theoretical analysis on more sophisticated optimization problems that entails using duality and convex analysis.

An Efficient Method for Identifying the Inactive Transmission Constraints in a Network-Constrained Unit Commitment

Network-constrained unit commitment(NCUC)is one of the most widely used applications in power system and electricity market operations.According to empirical evidence,some of the transmission constraints in a NCUC are inactive.Identifying and eliminating these inactive constraints can improve the efficiency.In this paper,an efficient method is first proposed for identifying the inactive transmission constraints.The physical and economic insights of NCUC are carefully considered and utilized.Both the generating costs and power transfer distribution factor(PTDF)are considered.Not only redundant constraints but also non-binding constraints can be identified via the proposed method.An acceleration method that combines relaxation-based neighborhood search and improved relaxation inducement is proposed for further reducing the computation time.The case study shows that the proposed method can significantly reduce the number of transmission constraints and substantially improve the efficiency of NCUC without impacting the optimality.

Energy and Exergy Efficiency Analysis of Advanced Adiabatic Compressed Air Energy Storage Based Trigeneration Energy Hub

With growing public awareness of decarbonization and increasing penetration of renewable generation,energy storage is in great need.Advanced adiabatic compressed air energy storage(AA-CAES)is capable of producing power,heating and cooling,making it an ideal choice of an environmental-friendly energy hub.This paper proposes an energy and exergy efficiency analysis for an AA-CAES based trigeneration energy hub.Impact of power storage and heat load supply rates on energy output efficiency and total exergy losses are analyzed.Based on the proposed model,optimal configuration of power storage and heat load supply rates can be determined under different purposes.According to basic thermodynamic principles,the proposed method calculates trigeneration capability estimates considering energy grade difference and multi-dimension energy distribution,which can demonstrate more energy conversion properties of the system.Case studies verify that the proposed method can provide various characteristic analyses for an energy hub and its application in actual systems proves computation accuracy.Integrative energy efficiency is improved compared to pursuing maximum electricity-to-electricity efficiency.

Market Equilibrium Based on Cloud-edge Collaboration

Market participants can only bid with lagged information disclosure under the existing market mechanism,which can lead to information asymmetry and irrational market behavior,thus influencing market efficiency.To promote rational bidding behavior of market participants and improve market efficiency,a novel electricity market mechanism based on cloudedge collaboration is proposed in this paper.Critical market information,called residual demand curve,is published to market participants in real-time on the cloud side,while participants on the edge side are allowed to adjust their bids according to the information disclosure prior to closure gate.The proposed mechanism can encourage rational bids in an incentive-compatible way through the process of dynamic equilibrium while protecting participants’privacy.This paper further formulates the mathematical model of market equilibrium to simulate the process of each market participant’s strategic bidding behavior towards equilibrium.A case study based on the IEEE 30-bus system shows the proposed market mechanism can effectively guide bidding behavior of market participants,while condensing exchanged information and protecting privacy of participants.

Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks

As numerous distributed energy resources(DERs)are integrated into the distribution networks,the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks(ADNs).Since accurate models are usually unavailable in ADNs,an increasing number of reinforcement learning(RL)based methods have been proposed for the optimal dispatch problem.However,these RL based methods are typically formulated without safety guarantees,which hinders their application in real world.In this paper,we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic(S3AC)for the optimal dispatch of DERs in ADNs,which not only minimizes the operational cost but also satisfies safety constraints during online execution.In the proposed S3AC,the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition(SCADA)system,effectively providing enhanced safety for executed actions.Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.

Integrated Distribution Management System:Architecture,Functions,and Application in China

As massive distributed energy resources(DERs)are integrated into distribution networks(DNs)and the distribution automation facilities are widely deployed,the DNs are evolving to active distribution networks(ADNs).This paper introduces the architecture and main function modules of an integrated distribution management system(IDMS)and its applica-tions in China.This system consists of three subsystems,including the real-time operation and control system(OCS),outage management system(OMS),and operator training simulator(OTS).The OCS has a hierarchical architecture with three levels,including the local controller for DER clusters,the optimization of DNs incorporated with multi-clusters,and the coordina-tion operation of integrated transmission&distribution(T&D)networks.The OMS is developed based on the geographical information system(GIS)and coordinated with OCS.While in the OTS,both the ADN and its host transmission network(TN)are simulated to make the simulation results more credible.The main functions of the three subsystems and their interaction data flows are described and some typical application scenarios are also presented.

An Efficient Decomposition Method for Bilevel Energy Storage Arbitrage Problem

With the reduction of cost,large-capacity energy storage unit is playing an increasingly important role in modern power systems.When a merchant energy storage unit participates in the power market,its arbitrage problem can be modeled via a bilevel program.The lower-level problem simulates power market clearing and gives the nodal price,based on which the upperlevel problem maximizes the arbitrage profit of energy storage.To solve this bilevel problem,the conventional method replaces the lower level problem with its KKT optimality conditions and further performs linearization.However,because the size of the market clearing problem grows with the scale of the power system and the number of periods,the resulting MILP(mixed-integer linear program)is very challenging to solve.This paper proposes a decomposition method to address the bilevel energy storage arbitrage problem.First,the locational marginal price at the storage connection node is expressed as a piecewise constant function in the storage bidding strategy,so the market clearing problem can be omitted.Then,the storage bidding problem is formulated as a mixed-integer linear program,which contains only a few binary variables.Numeric experiments validate the proposed method is exact and highly efficient.