Abstract:In recent years, power systems worldwide undergo dramatic changes in many respects of system operation, control and planning. With growing penetration of renewable energies and other emerging technologies, power grids today are facing various uncertainties and risks. Meanwhile, rapid development of smart grids demands innovative solutions to coordinate and optimize many new and old technologies of different properties to ensure overall system security and efficiency at large. To fulfill the need, this special issue is focused on applications of classical and emerging optimization techniques applied to solve the difficulties and challenges facing power system operation and planning in smart grid environments. Quite a number of high quality papers have been received. The covered topics are topical and broad.

Abstract:Optimal power flow (OPF) is one of the key tools for optimal operation and planning of modern power systems. Due to the high complexity with continuous and discrete control variables, modern heuristic optimization algorithms (HOAs) have been widely employed for the solution of OPF. This paper provides an overview of the latest applications of advanced HOAs in OPF problems. The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced, including genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and evolutionary programming (EP), etc.

Abstract:Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two schedu ling problems are commonly formulated with non-smooth cost functions respectively con-sidering various effects and constraints, such as the valve pointeffect, power balance and ramp rate limits. The expected increasein plug-in electric vehicles is li kely to see a significant impact onthe power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profil es are comparatively integratedinto a 24-hour load demand in an economic and environment dispatch model. Self-learning t eaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known bench mark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

Abstract:This paper presents the mean–variance (MV)model to solve power system reactive power dispatch problems with wind power integrated. The MV model considers the profit and risk simultaneously under the uncertain wind power (speed) environment. To describe this uncertain environment, the Latin hypercube sampling with Choleskyde composition simulation method is used to sample uncertain wind speeds. An improved optimization algorithm,group search optimizer with intraspecific competition andle ′ vy walk, is then used to optimize the MV model by introducing the risk to lerance parameter. The simulation is conducted based on the IEEE 30-bus power system, and there sults demonstrate the effectiveness and validity of the proposed model and the optimization algorithm.

Abstract:The increasing penetration level of photovoltaic(PV) power generation in low voltage (LV) networks resultsin voltage rise issues, particularly at the end of the feeders.In order to mitigate this problem, several strategies, such asgrid reinforcement, transformer tap change, demand-side management, active power curtailment, and reactive power optimization methods, show their contribution to voltage support, yet still limited. This paper proposes a coordinated volt-var control architecture between the LV distribution transformer and solar inverters to optimize the PV power penetration level in a representative LV network in Born-holm Island using a multi-objective genetic algorithm. The approach is to increase the reactive power contribution of the inverters closest to the transformer during over voltage conditions. Two standard reactive power control concepts,cosu(P) and Q(U), are simulated and compared in terms of network power losses and voltage level along the feeder. As a practical implementation, a reconfigurable hardware isused for developing a testing platform based on real-time measurements to regulate the reactive power level. The proposed testing platform has been developed within PVNET.dk project, which targets to study the approaches for large PV power integration into the network, with out the need of reinforcement.

Abstract:In the last two decades, the wind power generation has been rapidly and widely developed in many regionsand countries for tackling the problems of environmental pollution and sustainability of energy supply. However, the high share of intermittent and fluctuating wind power production has also increased the burden of system operator for securing power system reliability during the operational phase. Moreover, the power system restructuring and dereg-ulation have not only introduced the competition for reducing cost but also changed the strategy of reliability evaluation and management of power systems. The conventional long-term reliability evaluation techniques have been well developed,which have been more focused on planning and expansion rather than operation of power systems. This paper proposes anew technique for evaluating operational reliabilities of restructured power systems with high wind power penetration. The proposed technique is based on the combination ofthe reliability network equivalent and time-sequential simu-lation approaches. The operational reliability network equivalents are developed to represent reliability models of wind farms, conventional generation and reserve provides,fast reserve providers and transmission network in restruc-tured power systems. A contingency management schema forreal time operation considering its coupling with the day-ahead market is proposed. The time-sequential Monte Carlosimulation is used to model the chronological characteristicsof corresponding reliability network equivalents. A simplifiedmethod is also developed in the simulation procedures for improving the computational efficiency. The proposed tech-nique can be used to evaluate customers’ reliabilities considering high penetration of wind power during the power system operation in the deregulated environment.

Abstract:The reliability plays a significant role in power systems and it is an important objective or constraint intransmission expansion planning. Firstly, a DC optimization model was proposed to calculate the maximum arrivalpower at each load point. Compared to the network flow method, DC model is closer to the actual power flow and itis able to obtain more realistic reliability assessment results.Furthermore, a novel sensitivity index (SI) was also pro-posed to choose the most effective line so as to enhance thenodal and/or system reliability. The Monte Carlo simulationis 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 sys-tem. The results show the effectiveness of the method.

Abstract:With the development of smart meters, a real-time pricing (RTP) demand response is becoming possible for households in distribution networks. The power flowcan be bidirectional in distribution networks which become smarter with distributed generators (DGs). It is expensiveto import electricity from the generation far from load centers because of the cost of power loss and network use,so that it is more economical to use electricity generated bylocal distributed generators. Therefore, in order to curtailoperating costs of distribution networks, this paper proposes a model of economic optimization conducted bydistribution network operators. The electricity purchasing costs for distribution network operators are minimized byoptimizing electric power from transmission systems and local distributed generators. Further, based on price elas-ticity, the formulations of load demand considering RTP are proposed with economic optimization of distribution networks. The economic optimization problems are resolved by an interior point method. The case study shows that peak load demand can be reduced about 3.5% because the household RTP and electricity purchasing costs of distribution network operators can save 28.86 ￡ every hour.

Abstract:This paper proposes a new algorithm—binaryglowworm swarm optimization (BGSO) to solve the unitcommitment (UC) problem. After a certain quantity ofinitial feasible solutions is obtained by using the prioritylist and the decommitment of redundant unit, BGSO isapplied to optimize the on/off state of the unit, and the Lambda-iteration method is adopted to solve the economic dispatch problem. In the iterative process, the solutions thatdo not satisfy all the constraints are adjusted by the correction method. Furthermore, different adjustment techniques such as conversion from cold start to hot start,decommitment of redundant unit, are adopted to avoid falling into local optimal solution and to keep the diversityof the feasible solutions. The proposed BGSO is tested onthe power system in the range of 10–140 generating units for a 24-h scheduling period and compared to quantum-inspired evolutionary algorithm (QEA), improved binaryparticle swarm optimization (IBPSO) and mixed integer programming (MIP). Simulated results distinctly show that BGSO is very competent in solving the UC problem incomparison to the previously reported algorithms.

Abstract:In this paper, a mixed integer linear programming(MILP) formulation for robust state estimation (RSE) is proposed. By using the exactly linea rized measurement equations instead of the original nonlinear o nes, the existing mixed integer nonlinear programming formulation for RSE is converted to a MILP problem. The proposed approach not only guarantees to find the global optimum, but also does not have convergence problems. Simulation results on a rudimentary 3-bus system and several IEEE standard test sy stems fully illustrate that the proposed methodology is effective with high efficiency.

Abstract:Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC inter-action, a coordinated optimization method is presented in this paper. Firstly, a branch selection strategy is proposedby analyzing the sensitivity relationship between current limiting measures and the impedance matrix. Secondly, theimpact of network structure changes on the multi-infeed DC system is derived. Then the coordinated optimization model is established, which considers the cost and effect ofcurrent limiting measures, the tightness of network structure and the voltage support capability of AC system to multiple DCs. Finally, the non-dominated sorting geneticalgorithm II combining with the branch selection strategy,is used to find the Pareto optimal schemes. Case studies ona planning power system demonstrated the feasibility and speediness of this method.

Abstract:Demand side management techniques havedrawn significant attentions along with the development of smart grid. This paper proposes a new direct load control(DLC) model for scheduling interruptible air conditioner loads. The model is coordinated with the unit commitment and economic dispatch to minimize the total operation costover the whole dispatch horizon. The network constraints are also considered in the model. To ensure the thermal comfort of the occupants, we are among the first toincorporate the advanced two-parameter thermal inertiadynamical model of customer houses into the DLC modelto calculate the indoor temperature variation. This paperalso proposes a distributed imperialist competitive algo-rithm to effectively solve the model. The simulation studiesprove the efficiency of the proposed methodology.

Abstract:Particle swarm optimization (PSO) is one of the popular stochastic optimization based on swarm intelligence algorithm. This simple and promising algorithm has applications in many research fields. In PSO, each particle can adjustits ‘flying’ according to its own flying experience and its companions’ flying experience. This paper proposes a new PSO variant, called the statistically tracked PSO, which uses group statistical characteristics to update the velocity of the particle after certain iterations, thus avoiding local minima and helping particles to explore global optimum with an improved convergence. The performance of the proposed algorithm istested on a deregulated automatic generation control problemin power systems and encouraging results are obtained.

Abstract:Cascading failure is a potential threat in power systems with the scale development of wind power, especially for the large-scale grid-connected and long distance transmission wind power base in China. This introduces acomplex network theory (CNT) for cascading failure analysis considering wind farm integration. A cascading failure power flow analysis model for complex power networks is established with improved network topology principles and methods. The network load and boundary conditions are determined to reflect the operational states of power systems. Three typical network evaluation indicators are used to evaluate the topology characteristics of power network before and after malfunction including connectivity level, global effective performance and per-centage of load loss (PLL). The impacts of node removal,grid current tolerance capability, wind power instantaneous penetrations, and wind farm coupling points on the power grid are analyzed based on the IEEE 30 bus system.Through the simulation analysis, the occurrence mechanism and main influence factors of cascading failure are determined. Finally, corresponding defense strategies are proposed to reduce the hazards of cascading failure inpower systems.

Abstract:Due to the uncertainty and anti-peaking naturelarge scale integration of renewable energy imposes greatchallenges 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 fastregulating, flexible location and long-service life. Espe-cially, regenerative compressed air energy storage(RCAES) system is widely concerned as its capability ofheat recovery in the compression process. The cycle efficiency is a key indicator of RCAES system which can besignificantly impacted by the key parameters of the systemsincluding compression ratio, exhaust air pressure of throttle(EAPT) and the maximum working pressure (MWP) ofcompressed air storage vessel (CASV). However, currentresearch mostly focuses on the thermodynamic process andfew studies have focused on the impact of key parameters on RCAES system. Based on the efficiency evaluation method which was formulated through the electrical-mechanical-thermal dynamic process and measurable parameters, the impact of key parameters on the cycleefficiency of RCAES system is analyzed in this paper and apractical RCAES design scheme is adopted for case study

Abstract:A popular explicit analytic Borowy 2C PV module model is proposed for power generation prediction. The maximum power poi nt and the open-circuit point which are calculated in this model cannot be equalto the data given by manufacturers under standard test condition (STC). The derivation of this model has never been mentioned in any literatures. The parameter forms of 2Cmodel in this paper are more simplified,and the model is decomposed into a STC sub-model and an incrementalsub-model. The STC model is derived successfully froman ideal single-diode circuit model. Relative error esti-mations are developed to do the conformity error measurements. The analysis res ults showed that though the biases at those critical points are very small, the conformity will depend on both of the two ratio values Im/ IscandVm/ Voc,which can beusedtoverify whether 2C model is applicable for the PV module produced by a particular manufacturer.

Abstract:The network structure of the smart substation incommon use was introduced, and the technical problems of the shared-network of sampled measured value (SMV) and generic object oriented substation event (GOOSE) were analyzed, such as the processing ability of network deviceand the intelligent device, the data real-time property andthe network reliability, the effects to the substation in the condition of network fault, etc. On this basis, the feasibility of the shared-network of SMV and GOOSE was discussed,the implement scheme was presented, and eventually the solution of the shared-network of SMV and GOOSE wasput forward, which based on the applications of the message priority control, restricting the switch number, virtuallocal area network (VLAN) and GARP multicast registra-tion protocol (GMRP) classification flow control, flow ratelimiting, etc. In the test-bed, the cases of shared-network and separate-network of SMV and GOOSE were comparedand analyzed, and the result was valuable for reference.