Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation.The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization methods.However,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical foundations.This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used.In particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period problems.Lastly,we recommend some potential future directions for online optimization in the power systems domain.

Nonlinear disturbance attenuation control for four-leg active power filter based on voltage source inverter

Active power filter （APF） based on voltage source inverter （VSI） is one of the important measures for handling the power quality problem. Mathematically, the APF model in a power grid is a typical nonlinear one. The idea of passivity is a powerful tool to study the stabilization of such a nonlinear system. In this paper, a state-space model of the four-leg APF is derived, based on which a new H-infinity controller for current tracking is proposed from the passivity point of view. It can achieve not only asymptotic tracking, but also disturbance attenuation in the sense of L2-gain. Subsequently, a sufficient condition to guarantee the boundedness and desired mean of the DC voltage is also given. This straightforward condition is consistent with the power-balancing law of electrical circuits. Simulations performed on PSCAD platform verify the validity of the new approach.

Failure analysis on China power grid based on power law

This paper is concerned with the mechanism of blackouts in China power system from the viewpoint of self-organized criticality. By using two estimation algorithms of scaled window variance （SWV） and rescaled rangestatistics （R/S）, this paper studies the blackout data in China power system during 1988-1997. The result of analysis shows that the blackout data of 1994-1997 coincides well with the autocorrelation. Furthermore, it is found that the function of blackout probability vs. blackout size exhibits power law distribution.

Bifurcation suppression of nonlinear systems via dynamic output feedback and its applications to rotating stall control

This paper deals with the problems of bifurcation suppression and bifurcation suppression with stability of nonlinear systems. Necessary conditions and sufficient conditions for bifurcation suppression via dynamic output feedback are presented; Sufficient conditions for bifurcation suppression with stability via dynamic output feedback are obtained. As an application, a dynamic compensator,which guarantees that the bifurcation point of rotating stall in axial flow compressors is stably suppressed, is constructed.

Risk Constrained Self-scheduling of AA-CAES Facilities in Electricity and Heat Markets:A Distributionally Robust Optimization Approach

Advanced adiabatic compressed air energy storage(AA-CAES)has the advantages of large capacity,long service time,combined heat and power generation(CHP),and does not consume fossil fuels,making it a promising storage technology in a low-carbon society.An appropriate self-scheduling model can guarantee AA-CAES’s profit and attract investments.However,very few studies refer to the cogeneration ability of AA-CAES,which enables the possibility to trade in the electricity and heat markets at the same time.In this paper,we propose a multimarket self-scheduling model to make full use of heat produced in compressors.The volatile market price is modeled by a set of inexact distributions based on historical data through-divergence.Then,the self-scheduling model is cast as a robust risk constrained program by introducing Stackelberg game theory,and equivalently reformulated as a mixed-integer linear program(MILP).The numerical simulation results validate the proposed method and demonstrate that participating in multienergy markets increases overall profits.The impact of uncertainty parameters is also discussed in the sensibility analysis.

Editorial:special issue on analysis and control of complex systems in honor of the 90th birthday of Professor Huashu Qin

It is our great pleasure to organize this special issue in Control Theory and Technology in honor of the 90th birthday of Professor Huashu Qin.She is one of the pioneers in control theory and applications in China,and has made many truly outstanding contributions to the field including nonlinear dynamics and control,intelligent control,robotics and complex systems.It is no surprise that she was selected as a representative introduced significantly in“Women in Control”of IEEE Control Systems Magazine.

Capacity Allocation of Hybrid Power System with Hot Dry Rock Geothermal Energy,Thermal Storage,and PV Based on Game Approaches

This study utilizes hot dry rock(HDR)geothermal energy,which is not affected by climate,to address the capacity allocation of photovoltaic(PV)-storage hybrid power systems(HPSs)in frigid plateau regions.The study replaces the conventional electrochemical energy storage system with a stable HDR plant assisted by a flexible thermal storage(TS)plant.An HPS consisting of an HDR plant,a TS plant,and a PV plant is proposed.Game approaches are introduced to establish the game pattern model of the proposed HPS as the players.The annualized income of each player is used as the payoff function.Furthermore,non-cooperative game and cooperative game approaches for capacity allocation are proposed according to the interests of each player in the proposed HPS.Finally,the proposed model and approaches are validated by performing calculations for an HPS in the Gonghe Basin,Qinghai,China as a case study.The results show that in the proposed non-cooperative game approach,the players focus only on the individual payoff and neglect the overall system optimality.The proposed cooperative game approach for capacity allocation improves the flexibility of the HPS as well as the payoff of each game player.Thereby,the HPS can better satisfy the power fluctuation rate requirements of the grid and increase the equivalent firm capacity(EFC)of PV plants,which in turn indirectly guarantees the reliability of grid operation.

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.

An Integrated Control and Protection System for Photovoltaic Microgrids

The microgrid has shown to be a promising solution for the integration and management of intermittent renewable energy generation.This paper looks at critical issues surrounding microgrid control and protection.It proposes an integrated control and protection system with a hierarchical coordination control strategy consisting of a stand-alone operation mode,a grid-connected operation mode,and transitions between these two modes for a microgrid.To enhance the fault ride-through capability of the system,a comprehensive three-layer hierarchical protection system is also proposed,which fully adopts different protection schemes,such as relay protection,a hybrid energy storage system(HESS)regulation,and an emergency control.The effectiveness,feasibility,and practicality of the proposed systems are validated on a practical photovoltaic(PV)microgrid.This study is expected to provide some theoretical guidance and engineering construction experience for microgrids in general.

VSG-based Adaptive Droop Control for Frequency and Active Power Regulation in the MTDC System

In this paper,a VSG(virtual synchronous generator)-based method with adaptive active power and DC voltage droop is proposed for the control of VSC stations in the multi-terminal DC(MTDC)system.This control strategy can improve the inertial level of the AC networks and attenuate the rate of change of frequency when a disturbance occurs.In addition,the droop control of the active power and DC voltage is implemented to make the AC networks share the unbalanced power in the MTDC.The droop coefficients are adaptively adjusted depending on the frequency margin of every AC network,which makes the allocation of unbalanced power among AC networks more reasonable from the frequency variation perspective.The control strategy is evaluated in the scenarios of sudden load change and wind turbine tripping,and the results are presented to demonstrate its effectiveness.

Paving the Way to Smart Micro Energy Grid:Concepts,Design Principles,and Engineering Practices

The interconnection between initially independent energy infrastructures offers additional system flexibility and efficiency.The integration at distribution level simplifies the implementation of the integrated energy system functionalities.This paper proposes concepts and design principles of a smart micro energy grid(MEG)for accommodating micro-grids,distributed poly-generation systems,energy storage facilities,and associated energy distribution infrastructures.The energy management system is responsible for the smart operation of the MEG while supporting multiple criteria,such as safety,economy,and environmental protection.To realize the vision of the smart MEG,an engineering game theory based energy management system with self-approaching-optimum capability is investigated.Based on the proposed concepts,design principles,and energy management system,this paper presents a prototype of China’s first conceptual solar-based smart MEG,established in Qinghai University.

On engineering game theory with its application in power systems

Due to its capability of solving decision-making problems involving multiple entities and objectives, as well as complex action sequences, game theory has been a basic mathematical tool of economists, politicians, and sociologists for decades. It helps them understand how strategic interactions impact rational decisions of individual players in competitive and uncertain environment, if each player aims to get the best payoff. This situation is ubiquitous in engineering practices. This paper streamlines the foundations of engineering game theory, which uses concepts, theories and methodologies to guide the resolution of engineering design, operation, and control problems in a more canonical and systematic way. An overview of its application in smart grid technologies and power systems related topics is presented, and intriguing research directions are also envisioned.

Review and prospect of compressed air energy storage system

As an effective approach of implementing power load shifting,fostering the accommodation of renewable energy,such as the wind and solar generation,energy storage technique is playing an important role in the smart grid and energy internet.Compressed air energy storage(CAES) is a promising energy storage technology due to its cleanness,high efficiency,low cost,and long service life.This paper surveys state-of-the-art technologies of CAES,and makes endeavors to demonstrate the fundamental principles,classifications and operation modes of CAES.Critical subsystems of CAES are elaborated exhaustively.The application prospects and further research directions are summarized to promote the popularization of CAES in smart grid and energy internet.

Flexible unbalanced control with peak current limitation for virtual synchronous generator under voltage sags

Virtual synchronous generator(VSG)is gridfriendly for integrating distributed generations(DGs)since it can emulate the operation mechanism of traditional synchronous generator(SG).However,the traditional VSG control strategy,which is mainly suitable for balanced voltage conditions,may lead to power oscillations,current unbalance and even overcurrent under unbalanced voltage sags.To overcome this difficulty,a flexible unbalanced control with peak current limitation for VSG under unbalanced operating conditions is proposed.Based on the basic VSG control algorithm,the control strategy integrates two novel control modules,which are current reference generator(CRG)and power reference generator(PRG).The proposed control strategy can flexibly meet different operation demands,which includes current balancing,constant active or reactive power.And the injected currents are kept within safety values for a better utilization of the VSG power capacity.Furthermore,the experimental platform is built.Experimental results demonstrate the validness and effectiveness of the proposed control strategy.

Distributed Optimal Power Flow of DC Microgrids:A Penalty Based ADMM Approach

The popularity of direct current(DC)networks have made their optimal power flow(OPF)problem a hot topic.With the proliferation of distributed generation,the many problems of centralized optimization methods,such as single point failure and slow response speed,have led to utilization of measures such as distributed OPF methods.The OPF problem is non-convex,which makes it difficult to obtain an optimal solution.The second-order cone programming(SOCP)relaxation method is widely utilized to make the OPF problem convex.It is difficult to guarantee its exactness,especially when line constraints are considered.This paper proposes a penalty based ADMM approach using difference-of-convex programming(DCP)to solve the non-convex OPF problem in a distributed manner.The algorithm is composed of distributed x iteration,z iteration and A,/i iteration.Specifically,in the distributed z iteration,the active power flow injection equation of each line is formulated as a difference of two convex functions,and then the SOCP relaxation is given in a different form.If the SOCP relaxation is inexact,a penalty item is added to drive the solution to be feasible.Then,an optimal solution can be obtained using a local nonlinear programming method.Finally,simulations on a 14-bus system and the IEEE 123-bus system validate the effectiveness of the proposed approach.

Robust Unit Commitment for Large-scale Wind Generation and Run-off-river Hydropower

Increased penetration of large-scale renewable sources such as wind and solar power creates additional constraints for power system maintenance,reliability and security.The intermittent and fluctuation characteristics of renewable energy require robust operation methodologies.Co-optimized scheduling of multiple energy resources is regarded as an efficient way to accommodate renewable energy.As the second largest source of electricity in the world,hydro power is clean and has the merits of ease of regulation,low cost and high flexibility.In this regard,it can play an important role in mitigating the uncertainty of wind power.This paper proposes a robust wind-hydro-thermal unit commitment(UC)model that provides reliable day-ahead unit commitment decisions.The hydro units are operated in longer time scales than the thermal units,so as to capture reservoir operations.The hydro production capability curve is linearized by using variable separation and the piecewise linear(PWL)function,leading to a mixed integer linear programming(MILP)problem that can be solved using mature software such as CPLEX.Numerical tests on an IEEE 39-bus system demonstrate the effectiveness of the proposed model to increase system flexibility in accommodation of uncertain wind power.

Direct heuristic dynamic programming based on an improved PID neural network

In this paper, an improved PID-neural network （IPIDNN） structure is proposed and applied to the critic and action networks of direct heuristic dynamic programming （DHDP）. As one of online learning algorithm of approximate dynamic programming （ADP）, DHDP has demonstrated its applicability to large state and control problems. Theoretically, the DHDP algorithm requires access to full state feedback in order to obtain solutions to the Bellman optimality equation. Unfortunately, it is not always possible to access all the states in a real system. This paper proposes a solution by suggesting an IPIDNN configuration to construct the critic and action networks to achieve an output feedback control. Since this structure can estimate the integrals and derivatives of measurable outputs, more system states are utilized and thus better control performance are expected. Compared with traditional PIDNN, this configuration is flexible and easy to expand. Based on this structure, a gradient decent algorithm for this IPIDNN-based DHDP is presented. Convergence issues are addressed within a single learning time step and for the entire learning process. Some important insights are provided to guide the implementation of the algorithm. The proposed learning controller has been applied to a cart-pole system to validate the effectiveness of the structure and the algorithm.

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.

The impact of key parameters on the cycle efficiency of multi-stage RCAES system

Due to the uncertainty and anti-peaking nature,large scale integration of renewable energy imposes great challenges to the operation and dispatch of power systems.Compressed air energy storage(CAES)system provides new ideas to solve this problem as its characteristics of fast regulating,flexible location and long-service life.Especially,regenerative compressed air energy storage(RCAES)system is widely concerned as its capability of heat recovery in the compression process.The cycle efficiency is a key indicator of RCAES system which can be significantly impacted by the key parameters of the systems including compression ratio,exhaust air pressure of throttle(EAPT)and the maximum working pressure(MWP)of compressed air storage vessel(CASV).However,current research mostly focuses on the thermodynamic process and few studies have focused on the impact of key parameters on RCAES system.Based on the efficiency evaluation method which was formulated through the electricalmechanical-thermal dynamic process and measurable parameters,the impact of key parameters on the cycle efficiency of RCAES system is analyzed in this paper and a practical RCAES design scheme is adopted for case study.

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.