Robust optimization method for unit commitment with network losses considering wind uncertainties

-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.

In recent years, high penetration of variable generating sources, such as wind power, has challenged independent system operators (ISO) in keeping a cheap and reliable power system operation. Any deviation between expected and real wind production must be absorbed by the power system resources (reserves), which must be available and ready to be deployed in real time. To guarantee this resource availability, the system resources must be committed in advance, usually the day-ahead, by solving the so-called unit commitment (UC) problem. If the quantity of committed resources is extremely low, there will be devastating and costly consequences in the system, such as significant load shedding. On the other hand, if this quantity is extremely high, the system operation will be excessively expensive, mainly because facilities will not be fully exploited. This thesis proposes computationally efficient models for optimal day-ahead planning in (thermal) power systems to adequately face the stochastic nature of wind production in the real-time system operation. The models can support ISOs to face the new challenges in short-term planning as uncertainty increases dramatically due to the integration of variable generating resources. This thesis then tackles the UC problem in the following aspects: Power system representation: This thesis identifies drawbacks of the traditional energy-block scheduling approach, which make it unable to adequately prepare the power system to face deterministic and perfectly known events. To overcome those drawbacks, we propose the ramp-based scheduling approach that more accurately describes the system operation, thus better exploiting the system flexibility. UC computational performance: Developing more accurate models would be pointless if these models considerably increase the computational burden of the UC problem, which is already a complex integer and non-convex problem. We then devise simultaneously tight and compact formulations under the mixed-integer programming (MIP) approach. This simultaneous characteristic reinforces the convergence speed by reducing the search space (tightness) and simultaneously increasing the searching speed (compactness) with which solvers explore that reduced space. Uncertainty management in UC: By putting together the improvements in the previous two aspects, this thesis contributes to a better management of wind uncertainty in UC, even though these two aspects are in conflict and improving one often means harming the other. If compared with a traditional energy-block UC model under the stochastic (deterministic) paradigm, a stochastic (deterministic) ramp-based UC model: 1) leads to more economic operation, due to a better and more detailed system representation, while 2) being solved significantly faster, because the core of the model is built upon simultaneously tight and compact MIP formulations. To further improve the uncertainty management in the proposed ramp-based UC, we extend the formulation to a network-constrained UC with robust reserve modelling. Based on robust optimization insights, the UC solution guarantees feasibility for any realization of the uncertain wind production, within the considered uncertainty ranges. This final model remains as a pure linear MIP problem whose size does not depend on the uncertainty representation, thus avoiding the inherent computational complications of the stochastic and robust UCs commonly found in the literature.

One of the most important tasks for day-ahead electricity markets is to do the Security-Constrained Unit Commitment (SCUC). SCUC generally combines the needs of unit commitment, network limits, and regulation in a constrained optimization problem for different time frames. Different electricity markets utilize different SCUC methods. The main goal of this work is to introduce SCUC operation experience in several electricity markets and summarize significant issues in the current SCUC problem. For instance, in addition to traditional constraints in the SCUC process, this work also investigates the effect of frequency stability, transmission switching, or optimal generation scheduling on the SCUC methods. Additionally, the integration of renewable energy also bring the impact to the traditional SCUC methods. The above mentioned issues concerning the SCUC topic will be studied herein.

Given the development and utilization of large-scale wind power farms, the transfer ability of the power grid to accommodate wind power may limit efficient utilization of wind energy. In order to solve this problem, it is common to construct high-capacity transmission corridor to accommodate long distance cross-region wind power. In this paper, a scheduling model with the transmission and spinning reserve requirements is constructed considering the both wind power and load uncertainties. The impact of transmission corridor on cross region wind power penetration is discussed. The simulation results show that the construction of transmission corridor can enhance wind energy penetration and convert the resource advantages into economic advantages.

-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.

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.

Assuming that all electric vehicles (EVs) integrated to the grid are managed by aggregators, it is possible to make full use of the controllability of the charging EVs. Based on this, the charging load of each EV aggregator is defined as one of the optimal scheduling variables in the unit commitment (UC) model. This can be accomplished by assessing the upper and lower limit of charging load of each EV aggregator in different scenarios, which is simulated by Monte Carlo method. According to this, a UC model is proposed for optimal coordination of EV aggregators and wind generation, considering the EV owner's requirement, the range of charging load and the unit constraints. Using the10-units system as the test system, the results of UC models with different EVs charging strategies and the wind power integration indicators such as utilization rate are investigated. Simulation results show that both the abandoned wind power and the unit operating costs can be decreased more effectively in the proposed UC model.

Wind farm outputs have the features of intermittence and variability which impose a significant impact on the operation of power systems. In this paper, Latin hypercube sampling (LHS) and reduction technique is used to simulate the 24h power output of a wind farm. Then a model of unit commitment (UC) with predicted wind power (UCW) is established, and a harmony search (HS) with arithmetic crossover operation (ACHS) is employed for solving this problem. The results are analyzed in detail, which assess the impact of wind power on UC and demonstrate that ACHS is practicable for UCW problem with comparison with other proposed HS methods.

A method of short-term risk and economic dispatch of the hydro-thermal-wind-PV hybrid system considering spinning reserve requirements

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.

With the increasing penetration of wind power into power systems and the random fluctuation of the wind farm (WF) output, system flexibility must be considered in the optimal generation dispatch. Based on the extreme scenarios of the WF output, we proposed a flexibility risk index to evaluate the system flexibility of each time interval. We established a five-objective security-constrained unit commitment (SCUC) model of a power system with WFs, thermal generation plants, battery energy storage stations, and pumped storage hydro stations. The objectives were to minimize the system flexibility risk, total network loss, operation cost, power purchase cost, and pollutant gas emissions. To obtain the Pareto optimal solutions of the model, based on the objective selection and ε-constraint methods, we proposed two methods to reduce the dimension of objectives and transformed the five-objective optimization model into a series of three-objective optimization models. Then, we used the normalized normal constraint method to solve the Pareto frontier surface of each three-objective optimization model. We used a color column to represent the value change of the two upper layer objectives and visualized the Pareto frontier of the five-objective SCUC model in the three-dimensional ordinate space. Case studies on the modified IEEE-9 bus system and an actual power grid demonstrated the effectiveness and high computational efficiency of the proposed method.

Multi-step prediction of offshore wind power based on Transformer network and Huber loss

The world is experiencing a new round energy transition from fossil energy to clean energy. To many countries, continuously upgrading electrification level and accommodate more wind power and solar power to achieve re-electrification roadmap, will be a trend and the only way of these countries’ energy transition. In this paper, challenges brought by large-scale wind power and solar power to power system operation are analyzed from the aspects of accommodation, fluctuation characteristics, prediction error, power electronics characteristics and pulling down system marginal cost. Also, key issues of power market simulation methods, such as co-considered day-ahead market and real-time market by use of Security Constrained Unit Commitment (SCUC) model and Security Constrained Economic Dispatch (SCED) model respectively, co-optimized energy and ancillary service market and added rotational inertia verification in SCUC model, are studied.

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.

This paper presents a stochastic model for the long-term solution of security-constrained unit commitment (SCUC). The proposed approach could be used by vertically integrated utilities as well as the ISOs in electricity markets. In this model, random disturbances, such as outages of generation units and transmission lines as well as load forecasting inaccuracies, are modeled as scenario trees using the Monte Carlo simulation method. For dual optimization, coupling constraints among scenarios are relaxed and the optimization problem is decomposed into deterministic long-term SCUC subproblems. For each deterministic long-term SCUC, resource constraints represent fuel and emission constraints (in the case of vertically integrated utilities) and energy constraints (in the case of electricity markets). Lagrangian relaxation is used to decompose subproblems with long-term SCUC into tractable short-term MlP-based SCUC subproblems without resource constraints. Accordingly, penalty prices (Lagrangian multipliers) are signals to coordinate the master problem and small-scale subproblems. Computational requirements for solving scenario-based optimization models depend on the number of scenarios in which the objective is to minimize the weighted-average generation cost over the entire scenario tree. In large scale applications, the scenario reduction method is introduced for enhancing a tradeoff between calculation speed and accuracy of long-term SCUC solution. Numerical simulations indicate the effectiveness of the proposed approach for solving the stochastic security-constrained unit commitment.