Hourly Generation Schedule Research Articles

Particle Swarm Optimization for Short-Term Scheduling of Thermal-Hydro-Solar Power Generation Systems

Thermal-hydro-solar scheduling is the most difficult power system optimization issue in the modern day. The core mean of the arrangement of thermal-hydro-solar is to decide the most favorable power from thermal, hydro, and solar sources while meeting the various constraints of thermal, hydro, solar, and network. This paper describes the optimum hourly generation schedule plan in a thermal-hydro-solar power network utilizing particle swarm optimization (PSO) approach to attain the best or optimum solutions for scenarios involving three thermal power plants, four hydro power plants and ten solar photovoltaic (PV) plants. The conclusion of the simulation shows that the suggested PSO method seems to be able to minimize fuel costs, and emissions and has improved outcomes performance and strong integration than other approaches.

Innovative Unit Commitment Modeling Approach Considering Multi-infeed VSC-HVDC Links

This paper introduces an improved optimization model for the unit commitment (UC) problem of AC power systems considering multi-infeed VSC-HVDC links. The proposed formulation is based on the linear modeling of the AC network and point-to-point HVDC links, where power losses are properly considered by piecewise linearization of AC lines, DC links and VSC units. This UC model is featured by a mixed integer linear programming (MILP) framework, thus enabling effective calculations of the optimal hourly generation scheduling for multi-infeed VSC-HVDC power grids. These sophisticated power grid studies are associated with day-ahead electricity markets carried out by power system engineers at control centers. To demonstrate the applicability of the proposed MILP-based UC model, the IEEE RTS 24-bus test system is used to carry out two case studies. The first incorporates three point-to-point HVDC links where an in-depth analysis is carried by comparing its outcomes, with and without HVDC links. The second case study features two interconnected areas having three-infeed HVDC links and several tens of generation units. Both systems are analyzed for a 24-hr planning horizon. It is confirmed that the UC model described in this paper permits to study practical power grids with multi-infeed VSC-HVDC links.

Solution for short-term generation scheduling of cascaded hydrothermal system with turbulent water flow optimization

For economic operation of hybrid power systems, short-term hydrothermal planning is a significant operation which is focused to reduce the fuel costs of thermal power plants while fulfilling the different constraints of electrical and hydraulic networks. This paper addresses a newly developed algorithm, turbulent water flow optimization (TWFO) to solve the hydrothermal scheduling problem. TWFO is based on the whirlpool phenomenon which is formed through turbulent flow of water in seas or oceans. To enrich the solution quality of TWFO and to avoid trapping into local optimum, a chaotic logistic map is employed in the algorithm. The proposed TWFO is used to offer an optimal hourly generation schedule of hydrothermal power system considering the spillage effects of hydro plants, and the valve point loading effects, transmission losses and multi-fuel sources of thermal power plants. Besides, a repair mechanism is applied to fulfil all the equality and inequality constraints and to govern the solutions in the direction of feasible search space. The superiority of the proposed TWFO is examined on three hybrid power systems and the results of TWFO are compared with the performance of recent heuristic approaches including monarch butterfly optimization, Harris hawks optimization, red fox optimizer, remora optimization algorithm, student psychology-based optimization, and other erstwhile approaches reported in the literature. Numerical results demonstrate that the TWFO shows a superior performance in comparison with other approaches.

Short-term generation scheduling of cascade hydropower plants with strong hydraulic coupling and head-dependent prohibited operating zones

A lot of large hydropower plants have been built in China in the past few decades. Large-capacity hydropower units usually have multiple prohibited operating zones (POZs) varying with the net head. Many cascade hydropower plants are hydraulically coupled because the distance between the upstream and downstream reservoirs is so small that the forebay water level of the downstream reservoir will influence the tailwater level of its immediate upstream reservoir. The integrated consideration of the strong hydraulic coupling between cascade hydropower plants, the head-dependent POZs of individual units, and other operation constraints make the daily operation of cascade hydropower plants very challenging for both operators and researchers. Therefore, this paper has developed an accurate optimization model for determining the hourly generation scheduling of cascade hydropower plants with strong hydraulic coupling and head-dependent POZs. The objective is to maximize the total profits of the cascade hydropower plants from selling electricity in the day-ahead market. To solve such a complicated mathematical model with non-convex and nonlinear features, the model is converted into a mixed-integer linear programming (MILP) formulation using multiple linear approximation techniques so as to take full advantage of the effective and mature commercial solvers. The MILP formulation focuses mainly on addressing two nonlinearities, namely the three-dimensional tailwater level curves and head-dependent POZs, which are approximated through the piecewise linear interpolations based on the meshing and triangulation technique. The developed model is applied to the optimal operation of the Tianshengqiao cascade hydropower plants which are located on the Hongshui River, China. The optimization results demonstrate that the proposed model is able to be applied in the short-term generation scheduling of cascade hydropower plants in different hydrological conditions, and the optimized total profits for the cascade hydropower plants on typical days in dry season and flood season are 1,809,842 USD and 1,856,964 USD, respectively. Moreover, the developed model is computationally efficient and produces more realistic and executable generation scheduling than the state-of-the-art optimization model which only considers the head-dependent characteristics of POZs without considering the strong hydraulic coupling.

A novel decentralized method based on the system engineering concept for reliability‐security constraint unit commitment in restructured power environment

In the deregulated power environment, including Central operator (CO) and Micro Grids (MGs), different parts of the network are dedicated to the private sector, and each of them seeks to increase their profits independently. The CO and MGs should cooperate and collaborate in terms of operating, security and reliability in the whole power system. This article proposes a new method based on a System of System (SoS) concept for the secure and economic hourly generation scheduling to find optimal operational point. The main methodology includes three steps. In the first step, the power system is divided into several subsystems by using a spectral clustering partitioning technique to reduce converge time by decentralizes methods. And also load forecasting based on a Gaussian probability distribution function to avoid conventional calculation and considering uncertainty of the loads has been presented. To find a similar scenario, and reduction scenario with low probability, the probabilistic method has been addressed. The main contribution of this method is removing scenarios with low value of probabilities and scenarios which are similar to each other. In fact, the reduced set must include scenarios which are scattered appropriately in the uncertain space while holding high probabilities. In order to estimate the similarity (distance) between two scenarios the Kantorovich distance is implemented. In the second step, the hierarchical Bi-level optimization approach is used to execute the decentralized decision making to solve the Security Constraints Unit Commitment (SCUC) problem between CO and MGs. Regarding all physical relations and shared data among CO and MGs, the SoS concept and Bi-level optimization are presented to find the optimal operating point of autonomous systems. In the third step, a random number of generators will be select. Hence, the initial iteration number is set. In this step, sampling from state space to classifying reliability object achieved (The expected energy not supplied and loss of load probability are the reliability criterion). The presented method is evaluated using a 6-bus, the RTS 24-bus, 118-bus, and 4672-bus as an IEEE test systems. The suggested structure has been implemented by GAMS, and the results illustrate the usefulness of the presented methodology. To comparing proposed approach with the centralized method, the results illustrate improving total operational costs and security (in RTS-24($603,209), 118 bus ($2 562 154) and 4672-bus ($9 185 168)) in scenario 3 near to 9%, 10% and 8% respectively. Similarly, by comparison (in three test systems) with genetic algorithm these improvements are near to 23%, 22% and 13% respectively.

Considering the Differentiating Health Impacts of Fuel Emissions in Optimal Generation Scheduling

Thermal power generation could exhibit a considerable level of fuel emissions with a significant threat to human health. Due to variations in weather and atmospheric conditions, the time, the location, and the level of fuel emissions could determine emissions’ differentiating impacts on ambient air pollutant concentration (AAPC). The densely populated regions are more vulnerable to changes in AAPC because the associated health impacts can scale up log-linearly with AAPC and linearly with affected population sizes. This paper proposes an approach to evaluate the differentiating impacts of fuel emissions on AAPC and human health. The proposed approach applies the day-ahead hourly generation scheduling for reducing AAPC in densely populated regions. We employ a Gaussian puff model to address the source–receptor relationship among fuel emissions and resulting AAPC increments (ΔAAPC). We formulate a penalty cost for fuel emissions, which reflects the marginal health impacts of individual fuel emissions and differentiates the emission dispatch by shifting the electricity production away from power plants with high marginal health impacts. The proposed approach captures the ΔAAPC response to changes in generation scheduling and incorporates the marginal health impacts of fuel emissions into daily decision-making processes. Case studies demonstrate the effectiveness of the proposed model in which differentiating environmental benefits of wind power generation and energy storage are illustrated in power generation scheduling.

Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing

This paper presents a comparative study for five artificial intelligent (AI) techniques to the dynamic economic dispatch problem: differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing. Here, the optimal hourly generation schedule is determined. Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. The AI techniques for dynamic economic dispatch are evaluated against a ten-unit system with nonsmooth fuel cost function as a common testbed and the results are compared against each other.

Optimization Model for the Short-Term Operation of Hydropower Plants Transmitting Power to Multiple Power Grids via HVDC Transmission Lines

Long-distance and large-capacity hydropower transmission from large hydropower plants in southwestern China to load centers is an important and effective measure for the accommodation of large-scale hydropower in China. However, the load demands of the receiving-end power grids are not fully considered in the conventional power transmission mode, which has greatly affected their enthusiasm for the absorption of trans-regional hydro energy. In order to make full use of the peak shaving capability of the hydropower plants, this paper develops an optimization model for determining the hourly generation scheduling of the hydropower plants transmitting electric power to several power grids via high voltage direct current (HVDC) transmission lines. A large-capacity and highly complex multi-unit hydropower system, the Xiluodu plant in China, is taken as the case study. In the proposed model, minimizing the peak-valley differences of multiple receiving-end power grids is adopted as the objective to alleviate the peak shaving pressure of the receiving-end power grids. In addition to the traditional hydraulic constraints, the operation constraints of individual units and HVDC power transmission limits are well considered. The study focuses mainly on modeling the stair-like power transmission curve constraint which is discrete and nonlinear and has been rarely considered in previous studies. This constraint is then linearized through limiting the logical relations between multiple binary integer variables. Case studies demonstrate that the proposed model has high computational efficiency, and the peak-valley differences of the two receiving-end power grids, i.e., Zhejiang Power Grid and Guangdong Power Grid, are decreased by 15.2% and 7.33%, respectively. Moreover, frequent conversion of HVDC converter equipment can also be avoided, which makes the obtained generation schedule more executable.

Stochastic security-constrained operation of wind and hydrogen energy storage systems integrated with price-based demand response

Because of highly increasing energy consumption, environmental issues and lack of common energy sources, the use of renewable energy sources especially wind power generation technology is increasing with significant growth in the world. But due to the variable nature of these sources, new challenges have been created in the balance between production and consumption of power system. The hydrogen energy storage (HES) system by storing excess wind power through the technology of power to hydrogen (P2H) and delivering it to the electricity network through hydrogen-based gas turbine at the required hours reduces not only wind alternation but can play an important role in balancing power production and consumption. On the other hand, power consumers by participating in demand response (DR) programs can reduce their consumption at peak load or wind power shortage hours, and increase their consumption at low-load or excess wind power hours to reduce wind power spillage and system energy cost. This paper proposes a stochastic security constrained unit commitment (SCUC) with wind energy considering coordinated operation of price-based DR and HES system. Price-based DR has been formulated as a price responsive shiftable demand bidding mechanism. The proposed model has been tested on modified 6-bus and 24-bus systems. The numerical results show the effect of simultaneous consideration of HES system and price-based DR integrated with wind energy on hourly generation scheduling of thermal units. As a result there is some reduction in wind generation power spillage and daily operation cost.

Head Dependence of Pump-Storage-Unit Model Applied to Generation Scheduling

This paper presents a head-dependent model for pump storage units (PSUs) in a power system for short-term generation scheduling over one week. A hydraulic system with upper and lower reservoirs, each having their own in and out river flows, is considered. Hydraulic conditions, as well as head effects, are explicitly modeled for both generation and pumping modes of the PSUs. The problem is solved by the branch-and-cut method to obtain a near-optimal solution. Test results of an hourly generation schedule, including comparisons of pump efficiencies, hydraulic conditions, and operating costs of the PSUs for a real power system, are presented.

Robust hydrothermal scheduling under load uncertainty using information gap decision theory

Summary This paper investigates the application of a robust method to solve the problem of Daily Hydrothermal Generation Scheduling (DHGS). The Unit Commitment is implemented in restructured power systems by Independent System Operators to obtain an economic hourly generation schedule intended for the daily-ahead market. The application of Mixed-Integer Non-Linear Programming to deal with the DHGS problem has been discussed in this paper using Information Gap Decision Theory (IGDT) to assess a robust strategy for Independent System Operator. It is noted that the problem's uncertainty is due to load demand characterized using an IGDT method. The IGDT's concept is to model the uncertainty as an unbounded gap in the available information. Besides, this paper presents a model for risk-constrained DHGS to minimize the cost under uncertain load. This approach optimizes the robustness of decision-making strategy. Copyright © 2015 John Wiley & Sons, Ltd.

System of Systems Based Security-Constrained Unit Commitment Incorporating Active Distribution Grids

In restructured power systems, the transmission and distribution grids are autonomously utilized by independent system operator (ISO) and distribution companies (DISCOs), respectively. As the operating condition of one grid influences the decisions made by operators of other grids, the ISO and DISCOs should collaborate and cooperate with each other in order to operate the entire power system in a secure and economic manner. According to the concept of system of systems (SoS) engineering, this paper presents a decentralized decision-making framework to determine a secure and economical hourly generation schedule for a transmission system encompassing numbers of active distribution grids. Taking into consideration the physical connections and shared information between ISO and DISCOs, an SoS-based SCUC framework is designed and a hierarchical optimization algorithm is presented to find optimal operating points of all independent systems in the SoS-based power system architecture. The numerical results show the effectiveness of the proposed SoS framework and solution methodology.

Parallel hybrid enhanced inherited GA based scuc in a distributed cluster

In the deregulated electricity market, secure operation is an enduring concern of the independent system operator (ISO). For a secure and economical hourly generation schedule of the day ahead market, ISO executes the security constrained unit commitment (SCUC) problem. In this paper, a new formulation of SCUC problem, considering more practical constraints are presented. The proposed SCUC formulation includes constraints, such as hourly power demand, system reserves, ramp up/down limits, minimum ON/OFF duration limits. Unlike the traditional SCUC techniques the proposed method solves the Security Constrained Economic Dispatch (SCED) from the UC. To solve such SCUC model, a hybrid solution method consists of an enhanced inherited genetic algorithm (EIGA) is used for unit commitment master problem and Lambda relaxation method is used for the economic dispatch sub-problem. The message passing interface (MPI) based technique is used to implement the hybrid EIGA in distributed memory model. The time complexity and the solution quality with respect to the number of processors in a cluster are thoroughly analyzed. The effectiveness of the proposed method to solve the SCUC problem is shown on different test systems.

Particle swarm optimisation-based fuzzy satisfying method for economic environmental dispatch of hydrothermal power systems

A Particle Swarm Optimisation (PSO) based fuzzy satisfying method has been proposed to determine the optimal non-inferior hourly generation schedule of economic environmental dispatch of hydrothermal power system, consisting of multi-reservoir cascaded hydro plant with time delay and thermal plants with non smooth fuel cost and emission-level functions. This paper treats economy and emission as competing objectives for optimal dispatch, which requires some form of conflict resolution to arrive at a solution. The multiobjective problem is configured as a minimax problem under the assumption that the Decision-Maker (DM) has fuzzy goals for each of the objective functions. The present algorithm shows the suitability of employing the PSO method, a recently proposed combinatorial optimisation technique, in solving this problem. This random search based technique has been recently effectively employed in several application domains. The solution methodology can offer a global or near-global non-inferior solution for the DM. Numerical results for a sample test system have been presented to demonstrate the performance and applicability of the proposed method.

Comparison of Practical Unit Commitment Problem Solutions

In this article, a comparative study has been done for the unit commitment problem (UCP) with operational, power flow, and environmental constraints along with contingencies to plan a secure and economical hourly generation schedule based on the solutions obtained. The UCP solutions obtained without considering power-flow constraints are not practical due to secure operation of the power network. The UCP with power-flow constraints obtains the minimum system-operating cost, satisfying both unit and network constraints using hybrid Lagrangian multiplier and Newton–Raphson power-flow algorithms. Considering various environmental aspects, there is a need to include environment constraints to get a realistic unit commitment (UC) schedule. The UC solution for the environmentally constrained problem has been formulated as a multi-objective problem by considering both economic load dispatch and economic emission dispatch simultaneously. The combined economic emission dispatch bi-objective problem is converted to a single objective function by adding a modified price penalty factor. The UC schedule must withstand, even if there are any contingencies that have occurred in any line of the network, which can be incorporated by performing contingency analysis by removing one line from the system and performing optimal power flow (OPF) until all of the lines are removed once for each possible state. The state that converges for OPF for every line removal is selected, and OPF is performed again without any contingencies. In this article, the UC solutions obtained with operational, power flow, and environmental constraints, along with contingencies in the network, are compared for the IEEE 30-bus and the Indian utility practical systems scheduled for 24 hr. The algorithm and simulation are carried through a Matlab environment, and the results obtained are quite encouraging.

Fast SCUC for Large-Scale Power Systems

In restructured power systems, independent system operators (ISOs) or regional transmission organizations (RTOs) execute the security-constrained unit commitment (SCUC) program to plan a secure and economical hourly generation schedule for the daily/weekly-ahead market. As the size of ISO/RTO increases, the current SCUC algorithms could face critical challenges ranging from modeling accuracy to calculation complexity. This paper introduces an efficient fast SCUC (F-SCUC) for large-scale power systems. Main components used in the proposed approach include single-hour unit commitment with network security, single-hour unit commitment adjustment, unit commitment, economic dispatch, and hourly network security check. A reasonable and operational strategy for fixing and unlocking the generating units is explored. The iterative SCUC solution is controlled efficiently to accelerate the execution. Finally, the quality of F-SCUC solution is improved by executing a quick MlP-based SCUC solution according to the F-SCUC results. A 1168-bus system with 169 thermal units, a 4672-bus system with 676 thermal units, and two large systems with 1352 and 2704 thermal units are analyzed to exhibit the effectiveness of the proposed approach.

Security-Constrained Unit Commitment With AC Constraints*

In a restructured power market, the independent system operator (ISO) executes the security-constrained unit commitment (SCUC) program to plan a secure and economical hourly generation schedule for the day-ahead market. This paper introduces an efficient SCUC approach with ac constraints that obtains the minimum system operating cost while maintaining the security of power systems. The proposed approach applies the Benders decomposition for separating the unit commitment (UC) in the master problem from the network security check in subproblems. The master problem applies the augmented Lagrangian relaxation (LR) method and dynamic programming (DP) to solve UC. The subproblem checks ac network security constraints for the UC solution to determine whether a converged and secure ac power flow can be obtained. If any network violations arise, corresponding Benders cuts will be formed and added to the master problem for solving the next iteration of UC. The iterative process will continue until ac violations are eliminated and a converged optimal solution is found. In this paper, a six-bus system and the IEEE 118-bus system with 54 units are analyzed to exhibit the effectiveness of the proposed approach.

Security-Constrained Unit Commitment With AC Constraints

In a restructured power market, the independent system operator (ISO) executes the security-constrained unit commitment (SCUC) program to plan a secure and economical hourly generation schedule for the day-ahead market. This paper introduces an efficient SCUC approach with ac constraints that obtains the minimum system operating cost while maintaining the security of power systems. The proposed approach applies the Benders decomposition for separating the unit commitment (UC) in the master problem from the network security check in subproblems. The master problem applies the augmented Lagrangian relaxation (LR) method and dynamic programming (DP) to solve UC. The subproblem checks ac network security constraints for the UC solution to determine whether a converged and secure ac power flow can be obtained. If any network violations arise, corresponding Benders cuts will be formed and added to the master problem for solving the next iteration of UC. The iterative process will continue until ac violations are eliminated and a converged optimal solution is found. In this paper, a six-bus system and the IEEE 118-bus system with 54 units are analyzed to exhibit the effectiveness of the proposed approach.

Goal-Attainment Method Based on Simulated Annealing Technique for Economic-Environmental-Dispatch of Hydrothermal Power Systems with Cascaded Reservoirs

A goal-attainment method based on simulated annealing technique is applied to determine the optimal noninferior hourly generation schedule of economic-environmental-dispatch problem in a hydrothermal power system consisting of multi-reservoir cascaded hydro plants with time delay and thermal plants with nonsmooth fuel cost and emission level functions. In this article, cost and emission are treated as competing objectives. Assuming that the decision maker has goals for each of the objective functions, the multiobjective problem is converted into a single-objective optimization by a goal-attainment method which is then handled by a simulated annealing technique. The solution methodology can offer an optimal noninferior solution for the decision maker. Results of the application of the proposed method are presented.

Economic Generation Distribution Control Function (REG) in the Chilean Interconnected System

The Economic Generation Distribution Control function (REG) implemented at the Load Dispatch Center (LDC) of Empresa Nacional de Electricidad S.A. (ENDESA-CHILE) is described. ENDESA's generation is basically hydraulic. Based on short term studies the hourly generation schedule for each plant is prepared for every day 24 hours in advance. REG is a real time function intended to correct the generation schedule to take into account the difference between actual and predicted load and water inflows to the hydraulic plants. The REG function is implemented at two levels: at system level at the ENDESA's LDC and at plant level in the participating power plants with adecuate feedback between them. At system level the objective is to periodically distribute the error accumulated in the plant in charge of frequency control among the participating plants. At plant level the objective is to set the desired power determined at system level to optimize the use of water resources. The current state of the implementation is presented and further development are discussed.