Reliability Constrained Unit Commitment Research Articles

Enhancement in reliability‐constrained unit commitment considering state‐transition‐process and uncertain resources

The high penetration of uncertain resources challenges the security of power system operation. By taking the impact of rescheduling under contingencies into consideration, reliability-constrained unit commitment (RCUC) is developed to address this challenge. Although several efforts have been made in modelling reliability constraints, the existing methods can only manage oversimplified low-order temporal-independent contingencies without considering wide-range contingencies or their state-transition-process issue. To quantify the impact of rescheduling on the normal-state scheduling process denoted by UC problem, this paper builds up a Bayesian inference method for encoding reliability constraints in wide-range temporal-dependent contingencies. Three predictors, for example, expected-generator-rescheduling-power, expected-energy-not-serviced and lost-of-load-probability, are selected to describe the possible corrective behaviours in rescheduling process and quantified by using Bayesian inference method. Then, these predictors are reformatted as a set of linearized constraints to be incorporated into UC. The proposed RCUC comprehensively considers the effect of rescheduling in wide-range temporal-dependent contingencies. Therefore, it can reveal the influence of generator rescheduling in wide-range contingencies and keep better reliability performance than those methods reported in previous RCUC studies. The modified IEEE 30-bus test system and IEEE 118-bus test system are used to show the proposed model's effectiveness.

Reliability Constrained Unit Commitment with Electric Vehicles to Grid Using Hybrid Particle Swarm Optimization

Reliability Constrained Unit Commitment with Electric Vehicles to Grid Using Hybrid Particle Swarm Optimization

Cost allocation of spinning reserve based on risk contribution

Current cost allocation methods require generating companies (GENCOs) to afford spinning reserve (SR) costs according to their energy production rather than the impact on grid stabilization. The differences in generator reliability and forecast accuracy of renewables cause difficulty in quantifying the contribution of individual factors on the SR requirements (SRRs). First, this paper employs a reliability‐constrained unit commitment (RCUC) model to determine the SRR and SR costs according to the grid reliability. Then, a cost allocation method is proposed to allocate these SR costs based on risk contribution theories. The risk contribution theories, marginal contribution, and stand‐alone contribution are employed to measure the effect of individual risk factors on grid safety. The cost allocation method is demonstrated and discussed in the IEEE‐RTS. The proposed risk contribution method can quantify the impacts of risk factors on grid safety and allocate SR costs into them according to their contributions. Additionally, this risk‐based cost allocation method can encourage GENCOs to enhance the reliability level of generators and continuously improve the forecast accuracy of renewables to lower SR costs. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Comment on ‘Reliability constrained unit commitment with combined hydro and thermal generation embedded using self-learning group search optimizer’ by J.H. Zheng, J.J. Chen, Q.H. Wu, and Z.X. Jing [Energy 81 (2015) 245–254

This paper deals with the inaccuracy in results of the “Reliability constrained unit commitment with combined hydro and thermal generation embedded using self-learning group search optimizer proposed presented Energy 81 (2015) 245–254”. The hydrothermal scheduling problem is defined as a non-convex optimization problem, which is necessary to meet several equality and inequality constraints such as power balance and technical limitations of cascaded and thermal hydro units. The driven solutions in the original paper do not satisfy the constraint on the final storage volume of the hydro units' reservoir one at the last hour. Therefor, this paper demonstrates this inaccuracy situated in the original paper.

Reliability constrained unit commitment with combined hydro and thermal generation embedded using self-learning group search optimizer

This paper proposes a reliability constrained unit commitment problem with combined hydro and thermal generation embedded (RCHTUC), solved by a SLGSO (self-learning group search optimizer). The RCHTUC problem aims at minimizing the sum of fuel costs and start-up costs of thermal plants subject to various operation constraints. Furthermore, the problem takes into account the combination of hydro and thermal systems and the reliability constraints in hydrothermal power systems so as to respond to unforeseen outages and changes of load demands. In order to solve the RCHTUC problem, a SLGSO is developed from the GSO (group search optimizer), applying adaptive covariance matrix to design the optimum searching strategy and employing Lévy flights to increase the diversity of group. This paper reports on the simulation results obtained by the proposed method. The results are compared with those obtained by other methods on different hydrothermal systems over the scheduling horizon. The simulation results demonstrate the efficiency of the SLGSO for tackling the RCHTUC problem.

Reliability constrained unit commitment in smart grid environment

Maintaining the reliability in an acceptable level is one of the most important concerns of Independent System Operators (ISOs) in restructured power system. Reliability Constrained Unit Commitment (RCUC) would be executed by ISO in order to reach the reliable operation. Distributed Energy Resources (DERs) as emerging technologies of upcoming power system, could play a determinant role in reliability issues. As a result it is essential to apply some modifications on common RCUC formulation due to the impacts of DERs in order to make the power system operation more reliable, secure and economic. This paper presents a heuristic approach to solve RCUC problem considering the impacts of multiple DERs besides introducing Intelligent Distributed Energy Resources Operator (IDERO). IDEROs are new agents in smart grid environment with the responsibility of coordinating DERs in the most secure and reliable manner. The effectiveness of the proposed approach is illustrated by applying it to IEEE-RTS79.

Network and reliability constrained unit commitment problem using binary real coded firefly algorithm

In this paper, the binary real coded firefly (BRCFF) algorithm based on the flashing behavior of fireflies is proposed for solving network and reliability constrained unit commitment (UC) problem. In the first stage of the UC problem, the reliability constrained UC is solved using proposed binary coded firefly algorithm without satisfying network security constraints. The second stage of the problem solves optimal power flow (OPF) using real coded firefly algorithm to determine whether a converged and secure ac power flow can be obtained. Both the stages of the algorithm incorporate repair strategies and searches for a better solution. Here a random disturbance such as generation outages is modeled to evaluate the system reliability. Reliability of the system is recognized by loss of load probability (LOLP) index. The effectiveness of the proposed algorithm is demonstrated on 10 unit and IEEE RTS 24 bus system. The results of the proposed technique are compared with that of other techniques reported in the literature. In addition, the effect of the control parameters on the performance of firefly algorithm for UC problem is studied.

Unit Commitment With Probabilistic Spinning Reserve and Interruptible Load Considerations

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper demonstrates how a probabilistic criterion could be explicitly integrated in the unit commitment (UC) formulation. Both spinning reserve and interruptible load are taken into account as the operating reserve facilities. Incorporating the interruption notice time of interruptible load involves some complexities in the evaluation procedure and problem formulation which is considered in this paper. The expected energy not supplied (EENS) is considered as a probabilistic criterion and a new method is proposed to calculate this index in the presence of interruptible load. The reliability-constrained unit commitment (RCUC) problem is formulated based on the mixed-integer programming (MIP) method that can be solved using large-scale commercial solvers. The effectiveness of the proposed formulation is examined using the IEEE-RTS. Numerical results are presented and the implementation issues are discussed. A number of analyses are examined to evaluate the impact of some important factors on the problem solution. </para>

Reliability Constrained Unit Commitment Using Simulated Annealing

This paper proposes a new method for the incorporation of the unit unavailability and the uncertainty of the load forecast in the solution of the short-term unit commitment problem. The above parameters are taken into account in order to assess the required spinning reserve capacity at each hour of the dispatch period, so as to maintain an acceptable reliability level. The unit commitment problem is solved by a simulated annealing algorithm resulting in near-optimal unit commitment solutions. The evaluation of the required spinning reserve capacity is performed by implementing reliability constraints, based on the expected unserved energy and loss of load probability indexes. In this way, the required spinning reserve capacity is effectively scheduled according to the desired reliability level. Numerical simulations have proven the efficiency of the proposed method