State estimation is a key element in today's power systems for reliable system operation and control. State estimation collects information from a large number of meter measurements and analyzes it in a centralized manner at the control center. Existing state estimation approaches were traditionally assumed to be able to tolerate and detect random bad measurements. They were, however, recently shown to be vulnerable to intentional false data injection attacks. This paper fully develops the concept of load redistribution (LR) attacks, a special type of false data injection attacks, and analyzes their damage to power system operation in different time steps with different attacking resource limitations. Based on damaging effect analysis, we differentiate two attacking goals from the adversary's perspective, i.e., immediate attacking goal and delayed attacking goal. For the immediate attacking goal, this paper identifies the most damaging LR attack through a max-min attacker-defender model. Then, the criterion of determining effective protection strategies is explained. The effectiveness of the proposed model is tested on a 14-bus system. To the author's best knowledge, this is the first work of its kind, which quantitatively analyzes the damage of the false data injection attacks to power system operation and security. Our analysis hence provides an in-depth insight on effective attack prevention with limited protection resource budget.

Modeling Load Redistribution Attacks in Power Systems

http://ardeshir20.persiangig.com/document/noandishaan/azar/alex1.pdf

Optimal dynamic economic dispatch of generation: A review

Plug-in electric vehicles (PEVs) are increasingly popular in the global trend of energy saving and environmental protection. However, the uncoordinated charging of numerous PEVs can produce significant negative impacts on the secure and economic operation of the power system concerned. In this context, a hierarchical decomposition approach is presented to coordinate the charging/discharging behaviors of PEVs. The major objective of the upper-level model is to minimize the total cost of system operation by jointly dispatching generators and electric vehicle aggregators (EVAs). On the other hand, the lower-level model aims at strictly following the dispatching instructions from the upper-level decision-maker by designing appropriate charging/discharging strategies for each individual PEV in a specified dispatching period. Two highly efficient commercial solvers, namely AMPL/IPOPT and AMPL/CPLEX, respectively, are used to solve the developed hierarchical decomposition model. Finally, a modified IEEE 118-bus testing system including 6 EVAs is employed to demonstrate the performance of the developed model and method.

A Hierarchical Decomposition Approach for Coordinated Dispatch of Plug-in Electric Vehicles

Cyber security is becoming an area of growing concern in the electric power industry with the development of smart grid. False data injection attack, which is against state estimation through SCADA network, has recently attracted wide research interests. This paper further develops the concept of load redistribution (LR) attack, a special type of false data injection attack. The damage of LR attacks to power system operations can manifest in an immediate or a delayed fashion. For the immediate attacking goal, we show in this paper that the most damaging attack can be identified through a max-min attacker-defender model. Benders decomposition within a restart framework is used to solve the bilevel immediate LR attack problem with a moderate computational effort. Its effectiveness has been validated by the Karush-Kuhn-Tucker (KKT)-based method solution in our previous work. For the delayed attacking goal, we propose a trilevel model to identify the most damaging attack and transform the model into an equivalent single-level mixed-integer problem for final solution. In summary, this paper enables quantitative analysis of the damage of LR attacks to power system operations and security, and hence provides an in-depth insight on effective attack prevention when resource budgets are limited. A 14-bus system is used to test the correctness of the proposed model and algorithm.

Quantitative Analysis of Load Redistribution Attacks in Power Systems

Power electronic converter (PEC)-interfaced renewable energy generators (REGs) are increasingly being integrated to the power grid. With the high renewable power penetration levels, one of the key power system parameters, namely reactive power, is affected, provoking steady-state voltage and dynamic/transient stability issues. Therefore, it is imperative to maintain and manage adequate reactive power reserve to ensure a stable and reliable power grid. This paper presents a comprehensive literature review on the reactive power management in renewable rich power grids. Reactive power requirements stipulated in different grid codes for REGs are summarized to assess their adequacy for future network requirements. The PEC-interfaced REGs are discussed with a special emphasis on their reactive power compensation capability and control schemes. Along with REGs, conventional reactive power support devices (e.g., capacitor banks) and PEC-interfaced reactive power support devices (e.g., static synchronous compensators) play an indispensable role in the reactive power management of renewable rich power grids, and thus their reactive power control capabilities and limitations are thoroughly reviewed in this paper. Then, various reactive power control strategies are reviewed with a special emphasis on their advantages/disadvantages. Reactive power coordination between support devices and their optimal capacity are vital for an efficient and stable management of the power grid. Accordingly, the prominent reactive power coordination and optimization algorithms are critically examined and discussed in this paper. Finally, the key issues pertinent to the reactive power management in renewable rich power grids are enlisted with some important technical recommendations for the power industry, policymakers, and academic researchers.

Reactive Power Management in Renewable Rich Power Grids: A Review of Grid-Codes, Renewable Generators, Support Devices, Control Strategies and Optimization Algorithms

Competitive strategic bidding optimization is now a key issue in electricity generator markets. Digital ecosystems provide a powerful technological foundation and support for the implementation of the optimization. This paper presents a new strategic bidding optimization technique which applies bilevel programming and swarm intelligence. In this paper, we first propose a general multileader-one-follower nonlinear bilevel (MLNB) optimization concept and related definitions based on the generalized Nash equilibrium. By analyzing the strategic bidding behavior of generating companies, we create a specific MLNB decision model for day-ahead electricity markets. The MLNB decision model allows each generating company to choose its biddings to maximize its individual profit, and a market operator can find its minimized purchase electricity fare, which is determined by the output power of each unit and the uniform marginal prices. We then develop a particle-swarm-optimization-based algorithm to solve the problem defined in the MLNB decision model. The experiment results on a strategic bidding problem for a day-ahead electricity market have demonstrated the validity of the proposed decision model and algorithm.

Competitive Strategic Bidding Optimization in Electricity Markets Using Bilevel Programming and Swarm Technique

This paper proposes a new economic load dispatch model that considers cost coefficients with uncertainties and the constraints of ramp rate. The uncertainties are represented by fuzzy numbers, and the model is known as fuzzy dynamic economic load dispatch model (FDELD). A novel hybrid genetic algorithm with quasi-simplex techniques is proposed to handle the FDELD problem. The algorithm creates offspring by using generic operation and quasi-simplex techniques in parallel. The quasi-simplex techniques consider two potential optimal search directions in generating prospective offspring. One direction is the worst-opposite direction, which is used in the conventional simplex techniques, and the other is the best-forward direction, which is a ray from the centroid of a polyhedron whose vertexes are all the points but the best one towards the best point of the simplex. In addition, in order to reserve more fuzzy information, the fuzzy number ranking method is used to optimize the cost function, avoiding the lost some useful information by getting /spl lambda/-level set. The experimental study shows that FDELD is more practical model; the algorithm and techniques proposed are very efficient to solve FDELD problem.

Dynamic economic load dispatch using hybrid genetic algorithm and the method of fuzzy number ranking

Strategic bidding problems are becoming key issues in competitive electricity markets. This paper applies bilevel optimization theory to deal with this issue. We first analyze generating company strategic bidding behaviors and build a bilevel optimization model for a day-ahead electricity market. In this bilevel optimization model, each generating company will choose their bids in order to maximize their individual profits. A market operator will determine the output power for each unit and uniform marginal price based on the minimization purchase electricity fare. For solving this competitive strategic bidding problem described by the bilevel optimization model, a particle swarm optimization (PSO)-based algorithm is. Experiment results have demonstrated the validity of the PSO-based algorithm in solving the competitive strategic bidding problems for a day-ahead electricity market.

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A bilevel optimization model and a PSO-based algorithm in day-ahead electricity markets

This paper gives environmental/economic load dispatch model which considers cost and emission function coefficients with uncertainties and the constraints of ramp rate. The uncertainties are represented by fuzzy numbers, and the model is known as fuzzy dynamic environmental/economic load dispatch model (FDEELD). A novel weighted ideal point method (WIM) is proposed to solve the FDEELD problem. The WIM convert the FDEELD problem into the single objective fuzzy nonlinear programming problem. Hybrid evolutionary algorithm with quasi-simplex techniques is used to seek optimal solution of the single objective problem corresponding to the FDKKLD; fuzzy number ranking method is applied to compare fuzzy weighted objective function. The experimental results shows that FDEELD is more practical model; the algorithm and techniques proposed are efficient to solve the FDEELD problem.

Environmental/economic load dispatch based on weighted ideal point and hybrid evolutionary algorithm

This paper proposes a new constraint direct disposal method (GA-CDD), which need not penalty factor, is a universal method dealing with constraint. In order to increase convergence velocity and get global optimal solution, a skill of reserving some unfeasible solution was introduced. The experiment study has shown that the method proposed in this paper is very effective, especially the optimal solution locating in the border of the feasible region.

Guo-Li Zhang

Papers

Competitive Strategic Bidding Optimization in Electricity Markets Using Bilevel Programming and Swarm Technique

Dynamic economic load dispatch using hybrid genetic algorithm and the method of fuzzy number ranking

A bilevel optimization model and a PSO-based algorithm in day-ahead electricity markets

Environmental/economic load dispatch based on weighted ideal point and hybrid evolutionary algorithm

Real-Coded Genetic Algorithm for Constrained Optimization Problem