Multi-objective Security Research Articles

Multi-Objective Security Constrained Unit Commitment via Hybrid Evolutionary Algorithms

Multi-Objective Security Constrained Unit Commitment via Hybrid Evolutionary Algorithms

Security constrained economic dispatch: Considering contractual performance ability evaluating of power producers using TOPSIS method in spot market environment

In a high proportion of new energy environment, the influence brought by the high uncertainty of power generation side becomes increasingly prominent. The contractual performance ability of power producers directly affects the organizational difficulty of spot market. The entropy weight-TOPSIS method is used to comprehensively evaluate the ability of different power producers. According to the evaluation results, a day-ahead market multi-objective security constraint economic dispatch model is established, and the elitist non-dominated sorting genetic algorithm is used to solve the model. Taking IEEE New England 39-node system as an example, the rationality of the proposed evaluation method is verified, and the influence of considering the performance capability on each producer is analyzed in the market clearing stage. The results show that the competition of power suppliers in improving the reliability of power supply can be encouraged by considering the performance of power suppliers into the market clearing stage.

Research on cyberspace multi-objective security algorithm and decision mechanism of Energy Internet

The existing energy network security defense system uses firewall, intrusion detection, host monitoring, identity authentication, anti-virus software and vulnerability repair to build a fortress type rigid defense system to block or isolate external invasion. This static layered deep defense system is based on prior knowledge and has the advantages of rapid response and effective protection in the face of constant attacks When confronting the unknown attacking opponent, he is not able to do his best, and he is in danger of being attacked easily. In this context, multi-objective decision has more than two decision-making objectives and needs to use multiple criteria to evaluate and optimize the decision-making of alternatives. Due to the objectives of economic benefit, safety in production and environmental protection, it is necessary to use a variety of criteria to evaluate and optimize schemes. In this paper, we propose RBF neural network and weight-based algorithm to achieve multi-objective decision. We leverage RBF neural network to construct objective weight assignment model. The goal of our weight-based algorithm is that the multi-objective optimization problem is formulated as a single-objective optimization problem by assigning certain weights to each objective, and then the non-inferior solution of the multi-objective optimization problem is generated by changing the weights of each objective extensive.

A multi-objective voltage stability constrained energy management system for isolated microgrids

Nowadays, microgrids are more likely to operate in an isolated state, and it is of outmost importance to consider various security issues in the management of such systems. This paper proposes a new energy management system for isolated microgrids, which considers the voltage stability and generation contingency constraints. A multi-objective security constrained microgrid energy management system (MOSC-MEMS) based on a coordinated unit commitment-optimal power flow (UC-OPF) framework is introduced, aiming at minimization of real power losses, voltage deviations, and power generation costs. A Pareto optimal front is obtained using the ∊-constraint method, and the best compromise solution is derived using the Fuzzy Satisfying criterion. Furthermore, a CIGRE benchmark test system based on European medium voltage network is used to demonstrate the performance of the proposed MOSC-MEMS. The simulation results, obtained by implementing the proposed framework in general algebraic modeling system (GAMS), indicate the feasibility of the proposed model and imply the significance of security constraints in the microgrid energy management systems.

Multiobjective security game with fuzzy payoffs

A multiobjective security game problem with fuzzy payoffs is studied in this paper. The problem is formulated as a bilevel programming problem with fuzzy coefficients. Using the idea of nearest interval approximation of fuzzy numbers, the problem is transformed into a bilevel programming problem with interval coefficients. The Karush-Kuhn-Tucker conditions is applied then to reduce the problem to an interval multiobjective single-level problem. It is shown that the solutions of this problem are obtained by solving a single-objective programming problem. Validity and applicability of the method are illustrated by a practical example.

Multi-objective optimization and network security enhancement for congestion management in wholesale electricity market

This paper presents an effective approach for managing congestion in a power transmission system by optimally placing distributed generators at the load end. Congestion management involves multiple objectives such as elimination of congestion, minimum congestion cost and bus voltage limit violations. DG has been proved as viable source for removing the congestion problem of the transmission system. Optimal placement of DG has been done for decreasing locational marginal price, nodal congestion price thereby maximizing social welfare function and network security in deregulated power system. The multi-objective optimal power flow problem considered here is mainly for achieving balance between social welfare function and network security. Social welfare function relates with value of market which is the summation of producer and consumer surplus whereas network security relates mainly for security of transmission system. The proposed system lowers the overall price paid to market operators by assigning balanced weights to social welfare and network security enhancing the overall system performance.

Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO) algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF) problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators.

Multi-Objective Optimization for Distributed MIMO Networks

In this paper, we investigate the linear transceiver optimization for multiple-inputmultiple-output (MIMO) interference networks, where multiple pairs of multi-antenna source and destination nodes communicate simultaneously. Different from most of existing works, we jointly consider three critical issues of the linear transceiver optimization for MIMO interference networks based on multi-objective optimization theory, i.e., signal transmission, energy and security. Specifically, using the modified weighted Tchebycheff method, we investigate three kinds of multi-objective optimization problems (MOOPs): 1) sum mean square error minimization and harvested energy maximization; 2) transmit power minimization and energy harvesting efficiency maximization; 3) transmit power minimization, energy harvesting efficiency maximization, and physical layer security. Based on the Charnes–Cooper transformation and penalty function method, the formulated MOOPs are transformed into convex optimization problems and thus can be effectively solved. The resulting Pareto optimal solutions set reveals the complicated but important relationships among these involved single objective optimization problems, which are usually individually investigated in the literature. Finally, numerical simulation results demonstrate the performance advantages of the proposed algorithm and corroborate the theoretical analysis.

Multiobjective Dynamic Optimal Power Flow Considering Fuzzy-Based Smart Utilization of Mobile Electric Vehicles

In this study, an efficient and smart fuzzy logic controller (FLC) is designed for charging/discharging (C/D) nodes of plug-in electric vehicles (PEVs). The designed controller controls the amount of power to be compensated by these nodes in order to meet the required peak shaving and voltage flattening. The main focus of this work is to practically coordinate the mobile PEVs in a multiobjective security constrained dynamic optimal power flow (OPF) problem that aims at simultaneously minimizing the operation cost and emission over 24-h time horizons. In the formulation of the problem, nonsmooth, nonconvex, and nonlinear natures of valve-point effects, multifuel options, prohibited zones, and ac power flow equations are also considered. This complex problem needs a robust, fast, and powerful optimization algorithm, which is able to extract the Pareto-optimal surface (POS). Hence, a new improved black hole (IBH) algorithm is proposed with a new formulation for updating particles to allow a greater exploration and appropriate exploitation of the search space. The proposed framework is applied on IEEE 118-bus test system incorporating several aggregated PEVs to show its efficiency and ability.

Investigation of Multi objective Security Enhanced OPF with Transmission Pricing

In a deregulated environment, transmission pricing is very significant to allocate costs for different entities involved. In literature, Social benefit objective functions comprising optimal real power dispatch with transmission cost based problem explained. This paper proposes to determine solutions for OPF and transmission cost under multi objective framework. Security enhanced OPF and transmission pricing are the two objectives considered in this study. Transmission cost is calculated using Bialek's power flow tracing method. Non-dominated sorting genetic algorithm (NSGA-II), Multi objective differential evolution (MODE) and Multi objective particle swarm optimization (MOPSO) are utilised to obtain a solution for the problem. IEEE 30 bus and IEEE 118 bus systems are considered as test systems. Weighted sum based multi objective Human Group Optimization ((HGO) is used to find a reference pareto front.

Interactive DE for solving combined security environmental economic dispatch considering FACTS technology

This paper presents an efficient interactive differential evolution (IDE) to solve the multi-objective security environmental/economic dispatch (SEED) problem considering multi shunt flexible AC transmission system (FACTS) devices. Two sub problems are proposed. The first one is related to the active power planning to minimize the combined total fuel cost and emissions, while the second is a reactive power planning (RPP) using multi shunt FACTS device based static VAR compensator (SVC) installed at specified buses to make fine corrections to the voltage deviation, voltage phase profiles and reactive power violation. The migration operation inspired from biogeography-based optimization (BBO) algorithm is newly introduced in the proposed approach, thereby effectively exploring and exploiting promising regions in a space search by creating dynamically new efficient partitions. This new mechanism based migration between individuals from different subsystems makes the initial partitions to react more by changing experiences. To validate the robustness of the proposed approach, the proposed algorithm is tested on the Algerian 59-bus electrical network and on a large system, 40 generating units considering valve-point loading effect. Comparison of the results with recent global optimization methods show the superiority of the proposed IDE approach and confirm its potential for solving practical optimal power flow in terms of solution quality and convergence characteristics.

An extended study on multi-objective security games

The burgeoning area of security games has focused on real-world domains where security agencies protect critical infrastructure from a diverse set of adaptive adversaries. In such domains, decision makers have multiple competing objectives they must consider which may take different forms that are not readily comparable including safety, cost, and public perception. Thus, it can be difficult to know how to weigh the different objectives when deciding on a security strategy. To address the challenges of these domains, we propose a fundamentally different solution concept, multi-objective security games (MOSGs). Instead of a single optimal solution, MOSGs have a set of Pareto optimal (non-dominated) solutions referred to as the Pareto frontier, which can be generated by solving a sequence of constrained single-objective optimization problems (CSOPs). The Pareto frontier allows the decision maker to analyze the tradeoffs that exist between the multiple objectives. Our contributions include: (i) an algorithm, Iterative-?-Constraints,, for generating the sequence of CSOPs; (ii) an exact approach for solving an mixed-integer linear program (MILP) formulation of a CSOP; (iii) heuristics that achieve speed up by exploiting the structure of security games to further constrain the MILP; (iv) an approximate approach for solving a CSOP built off those same heuristics, increasing the scalability of our approach with quality guarantees. Additional contributions of this paper include proofs on the level of approximation, detailed experimental evaluation of the proposed approaches and heuristics, as well as a discussion on techniques for visualizing the Pareto frontier.

Multi-objective GA with fuzzy decision making for security enhancement in power system

Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.

Security Constrained Stochastic Multi-objective Optimal Power Dispatch

A stochastic multi-objective line security constrained problem is formulated to minimize non-commensurable objectives viz. operating cost, polluting gaseous emission and variance of active power generation and reactive power generation, with explicit recognition of statistical uncertainties in the thermal power generation cost coefficients, gaseous emission coefficients, power demands and hence power generations and bus voltages, which are considered random variables. Specific technique is put forth to convert the stochastic models into their respective deterministic equivalents. Fuzzy set theory has been exploited to evaluate the different objectives that are quantified by defining their membership functions. Security of transmission lines with respect to expected active power flow is considered in the form of fuzzy objective function. The solution set of such formulated problems is non-inferior due to contradictions among the objectives undertaken. The weighting method is used to simulate the trade-off relationship between the conflicting objectives in the non-inferior domain. Generally, the weights are either simulated or searched in the non-inferior domain. In the paper Evolutionary search technique is implemented to search the ‘preferred’ weightage pattern in the non-inferior domain, which corresponds to the ‘best’ compromised solution. Among the non-inferior solutions, the system operator selects the ‘preferred’ optimal operating point that provides maximum satisfaction level of the most under achieved objective in terms of membership function and is termed as fitness function. The validity of the proposed method has been demonstrated on an IEEE system comprising of 11-nodes, 17-lines and 5-generators.