Security Constraint Unit Commitment on Combined Solar Thermal Generating Units Using ABC Algorithm

Security-Constrained Unit Commitment (SCUC) problem and subsequently employing Flexible AC Transmission Systems (FACTS) controllers including Optimal Unified Power Flow Controller (OUPFC) play a crucial role in power systems operation and control in terms of increasing the security of energy transmission networks. The principal objective of the SCUC problem is to acquire the minimum operating cost simultaneously maintaining the security of the system. In this paper, significant attention is paid particularly to simultaneously control SCUC and OUPFC, since energy loss reduction and transmission lines capacity release can be effectively achieved with incorporating SCUC and OUPFC, and subsequently setting the effective variables. In this regard, the SCUC problem with the presence of OUPFC is solved as Nonlinear Programming with Discontinuous Derivatives (DNLP) framework and the proposed algorithm is implemented on two IEEE 14-bus and IEEE 30-bus systems using General Algebraic Modeling System (GAMS) software in order to optimize the total fuel cost, power losses and cost of OUPFC installation as objective functions in the multi-objective framework. The simulation results show the capability of the proposed algorithm to simultaneously determine the optimal location of the OUPFC and its settings in the SCUC problem and to improve the controllability of the power system operation.

In traditional electricity market unit commitment (UC) is done only for the reduction of operating cost. In the deregulated electricity market, for a secure and economical hourly generation schedule of the day ahead market, independent system operator (ISO) executes the security constrained unit commitment (SCUC) problem. The objective of the paper is to develop scheduling algorithm using hybrid particle swarm optimization (PSO) for electric generation that accounts directly for system security requirements. The proposed SCUC formulation includes constraints, such as hourly power demand, system reserves, ramp up/down limits, minimum ON/OFF duration limits. The innovation of our approach is to capture the system security requirements within a network flow sub-problem that is solved in conjunction with the unit commitment problem. To solve such SCUC model, a binary coded particle swarm optimization (BPSO) is used for unit commitment master problem and real coded particle swarm optimization (RPSO) is used for the economic dispatch sub-problem. The effectiveness of the suggested method to solve the SCUC problem is validated on different test systems.

The Branch-and-Bound (BnB) method is the fundamental solution framework for solving large-scale security-constrained unit commitment (SCUC) problem. Due to the central role variable selection rules play in such a solution procedure, this paper develops some efficient methods to actively learn the variable selection rule. Instead of using a pre-fixed rule, we propose to use a randomized strategy to select the branching variables. In such a strategy, the probability associated with the variable selection is learned from the historical solutions generated by the similar problems with different parametric patterns. To accelerate the learning procedure, we further propose to use either Grid Search or Bayesian Optimization technique to learn such a probability distribution. Using the randomly generated SCUC problems, we evaluate our randomized variable selection rule which incorporates the Most Infeasible Branching rule, Least Infeasible Branching rule, Pseudocost Branching rule, and the CPLEX Adaptive Branching rule as a basis. The preliminary computational results show that our proposed method gives remarkable improvements in solving the SCUC problem.

Security constrained unit commitment (SCUC) is one of the most important daily tasks that independent system operators (ISOs) or regional transmission organizations (RTOs) must accomplish in daily electric power market. Security constraints have long been regarded as difficult constraints for unit commitment problems. If the inactive security constraints can be identified and eliminated, the SCUC problem can be greatly simplified. In this paper, a necessary and sufficient condition for a security constraint to be inactive is established. It is proved that all inactive constraints can be identified by solving a series of small-scale mixed integer linear programming (MILP) problems. More importantly, an analytical sufficient condition is established and most of the inactive constraints can be quickly identified without solving MILP or linear programming (LP) problems. A very important feature of the conditions obtained is that they are only related to the load demands and parameters of the transmission network. Numerical testing is performed for three power grids and the results are impressive. Over 85% of the security constraints are identified as inactive and the crucial transmission lines affecting the total operating cost are among those associated with the remaining security constraints, providing useful information for transmission planning.

Considering the economics and securities for the operation of a power system, this paper presents a new adaptive dynamic programming approach for security-constrained unit commitment (SCUC) problems. In response to the “curse of dimension” problem of dynamic programming, the approach solves the Bellman’s equation of SCUC approximately by solving a sequence of simplified single stage optimization problems. An extended sequential truncation technique is proposed to explore the state space of the approach, which is superior to traditional sequential truncation in daily cost for unit commitment. Different test cases from 30 to 300 buses over a 24 h horizon are analyzed. Extensive numerical comparisons show that the proposed approach is capable of obtaining the optimal unit commitment schedules without any network and bus voltage violations, and minimizing the operation cost as well.

Out of step with the rapid growth of China's electricity load demand, the infrastructure of power grid in many areas is not strong enough to insure the security of power system operation. In this case, considering the strict N-1 conditions of lines and transformers, the situation that there is no feasible solution for the security-constrained unit commitment (SCUC) might occasionally occur. In order to address this issue, a homogenized-overload model based on SCUC is proposed in this paper. The homogenized-overload model could obtain a reasonable, suboptimal solution reducing the security risks by lowering the average overload level and reducing the number of overload lines without sacrificing the economy of unit commitment. To identify the overload line set, an identifying method based on the Benders decomposition algorithm is also proposed. The homogenized-overload model and the identifying method are tested respectively in a 6-bus system case and a real provincial power system in China. Simulation results show the validity of the model and the method.

Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.

This paper proposes solving short-term unit commitment (UC) problems with security constraints by hybrid chaotic particle swarm optimization (CPSO). The objective of security-constrained UC (SC-UC) is to minimize the total generation cost, which is the sum of both transition cost and production cost of the scheduled combination units. The proposed CPSO method, which involves the chaotic map and the chaotic turbulence, can avoid premature convergence of PSO and escape local minima. Moreover, the proposed hybrid CPSO method has the ability of solving both the continuous and the discrete optimal programming problems. Two power systems are demonstrated for verifying the feasibility and effectiveness of the proposed method in solving SC-UC problems with constraints of a nonlinear generator and operation security assessment taken into consideration. The simulation results show that the proposed CPSO-based SC-UC method is indeed capable of obtaining efficiently high-quality UC solutions and enhancing the system security of UC operation.

A novel robust security constrained unit commitment model considering HVDC regulation

-In this paper, Security-Constrained Unit Commitment (SCUC) model is proposed in a restructured power system. This model consists of a closed-loop modified Unit Commitment (UC) and Security-constrained Optimal Power Flow (SCOPF). The objective of this SCUC model is to obtain the maximum social welfare-based system operating cost while maintaining the system security. In conventional power systems, the demand was forecasted before market operation and determined as a fixed constant. Supplying this demand was therefore considered as a constraint. However, in restructured power system which is based on Standard Market Design (SMD), DISCOs offer the demand and their proposed prices; therefore the demand is modeled as an elastic load. Independent System Operator (ISO) is responsible for operating the power market. The ISO performed the power market using the SCUC software to obtain feasible and economical operation as much as possible. In this paper, Harmony Search Algorithm (HSA) has been implemented to solve SCUC problem. Since the SCUC problem is a non-linear, mixed integer, large scale and non-convex problem, harmony search optimization is addressed as an efficient technique to overcome the aforementioned challenges. The simulation results show that the presented method is both satisfactory and consistent with expectation.

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

This paper proposes an innovative genetic algorithm (GA) approach to solve the thermal unit commitment (UC) problem in power generation industry through a constraint satisfaction technique. Due to a large variety of constraints to be satisfied, the solution space of the UC problem is highly nonconvex, and therefore the UC problem can not be solved efficiently by the standard GA. To effectively deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of the other constraints are handled by integrating penalty factors into the cost function. Numerical results on the practical Taiwan Power (Taipower) system of 38 thermal units over a 24-hour period show that the features of easy implementation, fast convergence, and highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach. >

Unified solution of a non-convex SCUC problem using combination of modified Branch-and-Bound method with Quadratic Programming

-In this paper, Security-Constrained Unit Commitment (SCUC) model is proposed in a restructured power system. This model consists of a closed-loop modified Unit Commitment (UC) and Security-constrained Optimal Power Flow (SCOPF). The objective of this SCUC model is to obtain the maximum social welfare-based system operating cost while maintaining the system security. In conventional power systems, the demand was forecasted before market operation and determined as a fixed constant. Supplying this demand was therefore considered as a constraint. However, in restructured power system which is based on Standard Market Design (SMD), DISCOs offer the demand and their proposed prices; therefore the demand is modeled as an elastic load. Independent System Operator (ISO) is responsible for operating the power market. The ISO performed the power market using the SCUC software to obtain feasible and economical operation as much as possible. In this paper, Harmony Search Algorithm (HSA) has been implemented to solve SCUC problem. Since the SCUC problem is a non-linear, mixed integer, large scale and non-convex problem, harmony search optimization is addressed as an efficient technique to overcome the aforementioned challenges. The simulation results show that the presented method is both satisfactory and consistent with expectation.

This paper presents an algorithm for integrating and managing electrochemical energy storage systems (ESSs) on unit commitment (UC) problem. As some elements required for integrating electrochemical ESS, such as the power converter, have non-linear characteristics, its corresponding linear modeling could be difficult to be developed and included on the UC problem, which could lead to unfeasible solutions or unexpected results. In order to incorporate full models of ESS and its interface with the power system, in this paper an algorithm to incorporate electrochemical ESS management on the UC problem is presented. An insular power system of 10-units is analyzed, and conclusions are duly drawn.