Optimal Operation Problem Research Articles

RETRACTED: Economic, environmental, and reliability assessment of distribution network with liquid carbon-based energy storage using multi-objective group teaching optimization algorithm

In this paper, the optimum operation of a distribution network in the presence of renewable resources, and a new combined system of liquid carbon dioxide energy storage is investigated. A comprehensive structure is proposed for modeling and optimizing the combination of wind turbine and solar sources with the new energy storage system by considering their converters. Then, a multipurpose structure in the presence of these resources and the new storage resource is presented to overcome the uncertainty of the output power of renewable resources and improve the reliability of the grid. To this end, first, by considering the probabilistic nature of wind and solar resources, a relatively comprehensive modeling of the system is presented. Economic costs, including the cost of setting up units, cost of generation, and emission of these units, are presented as optimization problem. The effects of the storage system on the load curve are investigated and discussed. The proposed objective function is determined based on optimal load distribution and technical constraints. The multi-objective group teaching optimization algorithm is used to solve the microgrid optimal operation problem. The proposed model considering three cases, is successfully implemented on a 33-bus RBTS distribution network. The results indicated that the total cost of the proposed system in the first, second, and third case is decreased by about 2.88%, 21.78% and 21.2% compared to the base case study. The cost reduction in the first case is due to the use of renewable energy sources, and this reduction in the second case is more due to the use of new storage. Finally, in the third case, due to the consideration of uncertainty and reliability index, compared to the second case, the amount of cost has increased slightly, but compared to the main case, there is a significant decrease, which indicates the superiority of the proposed model.

Research on schedling optimization of four-way shuttle-based storage and retrieval systems

In this paper, we take the four-way shuttle system as the research object and establish the mathematical model of scheduling optimization based on the minimum time for the in/out operation optimization and path optimization scheduling problems of the four-way shuttle system. An improved genetic algorithm is used to solve the task planning, and an improved A* algorithm is used to solve the path optimization within the shelf level. The conflicts generated by the parallel operation of the four-way shuttle system are classified, and the improved A* algorithm based on the time window method is constructed for path optimization through the dynamic graph theory method to seek safe conflict-free paths. Through simulation example analysis, it is verified that the improved A* algorithm proposed in this paper has obvious optimization effect on the model of this paper.

Neurodynamics-based distributed model predictive control of a low-speed two-stroke marine main engine power system

This article studies a steady operation optimization problem of a low-speed two-stroke marine main engine (LTMME) power system including a cooling water subsystem, a fuel oil subsystem and a main engine subsystem with input and state constraints. Firstly, a distributed model with coupling inputs and states is established for the LTMME power system according to laws of thermodynamics and kinetics. Further, an optimization problem of the LTMME power system is formulated to ensure the system to operate steadily, subjected to constraint conditions of the distributed model and the input and state bounds. Moreover, the optimization problem is rewritten as a quadratic programming problem, and an iterative distributed model predictive control (DMPC) scheme based on a primal–dual neural network (PDNN) method is used to obtain the optimal inputs within the constrained range. Finally, based on the actual data from an underway ocean vessel named Mingzhou 501 with an LTMME power system, a group of simulations are carried out to verify the effectiveness of the proposed approach.

Boosting operational optimization of multi-energy systems by artificial neural nets

The operation of multi-energy systems has to be optimized repeatedly, e.g., to react to changing energy prices. Thus, operational optimization problems need to be solved in a reliably short time. Reliably short computations are challenging for optimizing multi-energy systems due to complex time-coupling constraints. These time-coupling constraints reflect effects such as component start costs or energy storage. However, time-coupling constraints increase the computational effort.Here, we present a decomposition method to solve the operational optimization using artificial neural nets efficiently. The method decomposes the operational optimization into single-time-step optimizations. The single-time-step optimizations incorporate predictions from artificial neural networks trained on long-term operational optimizations.In two case studies, the method provides high-quality solutions for all operational optimization problems in less than 2min. The method is significantly faster up to a factor of 375 than directly solving the operational optimization problem while practically retaining the quality of the solution.

Graph convolutional network-based security-constrained unit commitment leveraging power grid topology in learning

Security-constrained unit commitment (SCUC) is a complex optimization problem in power system operation, which is computationally intensive. To bring significant time-savings, this paper presents a graph convolutional network (GCN)-based SCUC approach (GCN-SCUC) using the information of power grid topology. Instead of tackling the mixed integer linear programming (MILP)-based SCUC (MILP-SCUC), the GCN learner predicts the unit decisions first, and then the MILP-SCUC problem is transformed into a continuous convex one. Numerical experiments are performed on the modified IEEE-30 and IEEE-118 systems to verify the feasibility of our approach both in terms of accuracy and computation time. Moreover, compared with the state-of-the-art MILP-SCUC, the proposed approach achieves speedups of between 13x and 17x on different testing examples with high accuracy.

Coordinated Operation of Fixed-Route and Demand-Responsive Feeder Transit Services in a Travel Corridor

With the wide use of smartphone travel apps, a new hybrid transit system (HTS) that integrates fixed-route transit (FRT) and app-based demand-responsive feeder transit (DRFT) services have recently emerged in many cities over the world. App-based DRFT services are coordinated with FRT to offer fast, comfortable, and well-connected mobility services to travelers. The study investigates a new variant of HTS, termed flexible HTS (FHTS), allowing FRT to have flexible vehicle departure times and capacitated vehicle loads and DRFT to have flexible feeder stations, so as to further enhance the coordination between FRT and DRFT. The coordinated operation optimization problem of such an FHTS in a travel corridor is formulated as a mixed-integer linear programming model. A novel hybrid metaheuristic algorithm is developed to solve it. In the hybrid algorithm, a constrained compass search method is used to optimize FRT vehicle departure times, and an adaptive large neighborhood search algorithm, coupled with a tailored forward time slack algorithm, is applied to solve the DRFT routing and scheduling problems. A case study in Shucheng County, China is conducted to examine the performance of the model and solution algorithms. Sensitivity analyses on feeder station layout and vehicle size are also conducted. The results show that flexible FRT vehicle departure times and flexible DRFT feeder stations have a great potential of enhancing the coordination of FRT and DRFT services by reducing operators’ costs and passengers’ total travel time. Managerial implications are to set feeder transit stations downstream of an FRT line and to keep a reasonable station spacing.

Stochastic multi-objective optimal sizing of battery energy storage system for a residential home

The deployment of renewable local generation sources at home can help reduce emission contributions within residential homes. Furthermore, the adoption of the residential PV system is increasing as the economics of installation continues to decrease. An optimally sized battery energy storage system can help maximise the benefits of the power generated from the PV systems while being economical. In this paper, a stochastic optimisation problem is formulated to determine the optimal size of the energy storage system and investigate the benefits of uncertainty consideration in the formulation of the sizing optimisation problem. A multi-objective problem is formulated consisting of two objectives: minimise the cost of purchasing the battery energy storage system, and minimise the amount of energy imported from the grid within the period. The stochastic problem is formulated as a two-stage scenario stochastic problem. The optimal sizing and operation problem was formulated as a mixed-integer linear programming problem and solved using the CPLEX solver. This work also utilises a Monte Carlo approach to deal with the uncertainty in load forecasting. Simulation results show that the proposed approach can estimate an optimal battery energy storage system at the current cost of BESS and clearly indicate the benefit of a stochastic approach.

Joint Optimization and Learning Approach for Smart Operation of Hydrogen-Based Building Energy Systems

In recent years, hydrogen-based multi-energy systems (HMESs) have received wide attention. However, existing works on the optimal operation of HMESs neglect building thermal dynamics, which means that the flexibility of thermal loads can not be utilized for reducing system operation cost. In this paper, we investigate an optimal operation problem of an HMES with the consideration of building thermal dynamics. Specifically, we first formulate an expected operational cost minimization problem related to an HMES. Due to the existence of uncertain parameters, inexplicit building thermal dynamics models, spatially and temporally coupled operational constraints, and nonlinear constraints, it is challenging to solve the formulated problem. Then, we propose an algorithm to solve the problem based on model-based optimization and data-driven based learning. The key idea of the proposed algorithm is summarized as follows: (1) transforming the long-term cost minimization problem into several single-slot subproblems using Lyapunov optimization techniques; (2) dividing each single-slot subproblem into two parts according to the availability of model information; (3) solving one part based on convex optimization and solving another part using multi-agent attention-based deep deterministic policy gradient. Simulation results based on real-world traces show the effectiveness of the proposed algorithm.

Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow

This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimization of three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid-connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín-Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times.

Toward transactive control of coupled electric power and district heating networks

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits.

Optimal Power Flow with Stochastic Solar Power Using Clustering-Based Multi-Objective Differential Evolution

Optimal power flow is one of the fundamental optimal operation problems for power systems. With the increasing scale of solar energy integrated into power systems, the uncertainty of solar power brings intractable challenges to the power system operation. The multi-objective optimal power flow (MOOPF) considering the solar energy becomes a hotspot issue. In this study, a MOOPF model considering the uncertainty of solar power is proposed. Both scenarios of overestimation and underestimation of solar power are modeled and penalized in the form of operating cost. In order to solve this multi-objective optimization model effectively, this study proposes a clustering-based multi-objective differential evolution (CMODE) which is based on the main features: (1) extending DE into multi-objective algorithm, (2) introducing the feasible solution priority technique to deal with different constraints, and (3) combining the feasible solution priority technique and the merged hierarchical clustering method to determine the optimal Pareto frontier. The simulation outcomes on two cases based on the IEEE 57-bus system verify the reliability and superiority of CMODE over other peer methods in addressing the MOOPF.

Virtual smart energy Hub: A powerful tool for integrated multi energy systems operation

The present work introduces a novel unit, named virtual smart energy hub (VSEH). VSEH consists of a combination of smart energy grids, smart energy hubs (SEHs), and virtual power plants (VPPs) and simultaneously benefits from the advantages of SEHs and VPPs on a smart-energy-grid platform. A comprehensive two-stage model was proposed to solve the VSEH optimal operation problem in order to trading in day-ahead and intraday heat and power market. The proposed stochastic programming model is risk based and the conditional value at risk (CVaR) index was employed to represent the risk in the scheduling model. On the first stage of the proposed model, VSEH schedules transactions amounts by considering energy prices and SEHs constraints in order to maximize profits from participation in day-ahead heat and power markets. In the second stage, VSEH, through the more accurate prediction of uncertainties before operation time and with the aim of maximizing profits and correcting deviations relative to the settled transactions in the first stage, re-schedules the SEHs in intraday heat and power markets. Non-arbitrage constraints were established between day-ahead and intraday energy markets in the proposed model. Moreover, the presented structure was modeled and comprehensively defined in mathematical terms as a mixed-integer linear programming (MILP) model with a minimum number of real variables. The performance of this model was evaluated by implementing it on a coalition with various SEHs, as a result of which the profitability of the coalition and the increase in the risk tolerance level of the SEHs group was confirmed. The results indicate that formation of the VSEH can act as means of risk management in addition to reducing risk-associated costs regardless of the circumstances on the day of operation and the acceptable level risk tolerance of the SEHs. In other words, the integration of SEHs will result in a lower risk aversion cost.

Day-ahead optimal operation of active distribution networks with distributed generation and energy storage

Abstract In this work we address the optimal operation in active distribution networks (ADNs) with high penetration of renewable energies and energy storage. The optimal performance of ADNs can include two different optimization problems: Unit Commitment (UC) and Economic Dispatch (ED). The UC problem determines the start-up and shutdown planning of all the dispatchable generation units to supply the electricity demand, minimizing the total cost of operation, while the ED problem determines the active output power of each of the committed units for each hour of the planning horizon. Both problems have the objectives of minimizing the total cost, supplying the demand and complying with the restrictions of the main network. Here the two problems are solved together to achieve the day-ahead optimal operation of active distribution networks with distributed generation and energy storage. A test system based on the IEEE 33-bus distribution network was proposed. The optimal operation problem presented here is analyzed using four scenarios with different renewable generation and load conditions and a time-varying profile for the purchase price of energy from the network. The results reveal that the proposed network together with the optimization methodology can face diverse and highly demanding load situations, with the full use of renewable energies and complying with all the restrictions imposed. The proposed methodology is suitable for use in other optimization problems such as determining the sizing of storage units and distributed generation.

Reservoirs Optimal Operation Based on Reinforcement Learning

Reinforcement learning has been widely used to solve the problem of optimal operation of a single reservoir. However, the research on the combination of reinforcement learning and optimal operation of reservoir groups is still blank. The complexity of reservoir groups brings difficulties to the application of reinforcement learning. In order to solve the complex problem of optimal operation of the reservoir group, Q-learning combined with the penalty method is used to generate the optimal operation scheme of the reservoir group by exploring the optimization Q value of the operation sequence. The experimental results show that this method can achieve the optimal solution in theory. Furthermore, Q-learning combined with a feasible direction method is used to establish time feasible state table and state-feasible action hash table according to the constraints of the discrete four-reservoirs problem, so that the Q-learning algorithm can shorten the optimization time and obtain the optimal solution.

Comparison of multi-objective genetic algorithms for optimization of cascade reservoir systems

Abstract Multi-objective genetic algorithms (MOGAs) are widely used for multi-reservoir systems’ optimization due to their high efficiency and fast convergence. However, the computational cost grows exponentially with the expansion of multi-reservoir systems and the increased dimensions of optimization problems, posing a great challenge to multi-reservoir operations. Therefore, it is important to find a suitable and efficient multi-objective algorithm for multi-reservoir system optimization. In this study, three representative MOGAs were selected, namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-Dominated Sorting Genetic Algorithm III (NSGA-III), and Reference Vector Guided Evolutionary Algorithm (RVEA), and three multi-objective optimization models were then developed accordingly. Numerical experiments were conducted to evaluate the performance of these three algorithms for the optimization of a cascaded reservoir system. The results show that for the two-objective model, the non-dominant size generation rate (NDSGR) of NSGA-II is 1.66 and 4.01 times that of NSGA-III and RVEA, respectively. NSGA-II is more suitable for solving the optimization operation problem with two objectives. Based on the comprehensive evaluation, NSGA-III seems to be more appropriate for more than two objectives. These results emphasize the importance of using appropriate algorithms and provide further insights into the choice of algorithms for the optimization of hydropower systems.

Unified operation optimization model of integrated coal mine energy systems and its solutions based on autonomous intelligence

An integrated coal mine energy system involves the production, transmission, conversion, storage, and consumption of multiple types of energy with complicated coupling relationships. The operation optimization problem of this system is characterized by multi-scenario, multi-variable, multi-objective, and strong constraints, making it difficult to be solved. Based on the structure of this system, a unified system operation optimization model suitable for various scenarios is established to minimize the economic and carbon transaction costs. To effectively solve optimization problems in various scenarios, we propose an autonomous intelligent optimization strategy based on a support vector machine to make full use of their characteristics in various scenarios to generate the most target intelligent optimization algorithms. To improve the performance of a population in convergence under strong constraints, we develop three strategies for repairing infeasible solutions according to specific preferences. Taking a mine in Shanxi Province as the object under test, a series of experiments are conducted under four typical scenarios, and the experimental results show the effectiveness of the proposed algorithm.

A subset-selection-based derivative-free optimization algorithm for dynamic operation optimization in a steel-making process

ABSTRACT Steel-making production is a dynamic process that has the characteristics of high temperature and heat, and a complex reaction mechanism that causes the mechanism model possibly to be unavailable and the system to be a black box. In this article, a dynamic operation optimization (DOO) problem is refined from the basic oxygen furnace (BOF) steel-making process, and the system model is formulated by a data analytics method. This prevents to solve the optimization problem with derivative-based optimization methods. To circumvent these difficulties, a surrogate-model-based derivative-free optimization algorithm is proposed for solving the DOO problem. In order to establish the surrogate model with the least number of function evaluations, a subset selection strategy is designed to find a sparse structure for the optimization problem, based on which a set of simple bases is determined to establish the surrogate model. Moreover, this also reduces the scale of the parameter optimization problem. Numerical experiments on actual production data verify the applicability and effectiveness of the proposed method.

Analysis of Optimal Operation of Charging Stations Based on Dynamic Target Tracking of Electric Vehicles

In view of the large impact of traditional charging stations on the power grid and the investment in the construction of charging stations for electric vehicle infrastructure services, this paper considers the configuration of optical storage equipment in charging stations from a practical point of view and proposes an economic operation strategy for charging stations to meet the economically optimal requirements of different scenarios. First, we analyze the behavioral characteristics of multiple types of electric vehicles, consider the influence of charging queues, and establish a daily load model of charging stations by taking into account the daily monitoring load and nighttime lighting load of charging stations. Then, considering the electric vehicle (EV) charging demand, photovoltaic (PV) output and energy storage charging and discharging power, the daily economic optimal operation problem based on the dynamic target tracking of charging stations is established; the objective is to maximize the daily operating revenue of charging stations under the condition of satisfying the EV charging demand and PV consumption. Secondly, the objective function is linearized, and the economic operation model is transformed into a mixed integer linear programming model for solving, and the simulation is verified under different scenarios. The results show that the economic optimal operation strategy can adapt to the economic operation requirements of charging stations in different scenarios and maximize the charging station revenue.

A bi-level co-expansion planning of integrated electric and heating system considering demand response

With the wide deployment of combined heat and power units, electric boilers, etc., the power system and the heating system are coupled tightly, which necessitates expansion planning in a coordinated manner. Demand response (DR) is considered an effective method for augmenting system flexibility, which would lead to a more beneficial planning strategy in the co-expansion planning strategy. Therefore, we develop a bi-level co-expansion planning model with DR constraints for the integrated electric and heating system to minimize expenses on both investment and operation. The upper level gives the optimal investment strategy of energy facilities, while the lower level is optimal operation problems with DR constraints under the given investment decision. Numerical simulation is employed in the P6H8 system to demonstrate the proposed model.

Enhancements to explicit stochastic reservoir operation optimization method

The Fletcher-Ponnambalam (FP) method is an explicit stochastic optimization method for design and operations management of real-world storage systems including surface water reservoir and groundwater management problems. The FP method faces no curse of dimensionality and no need for scenario generation. The paper introduces a novel implementation for the FP method, named FP-2022 here for clarity, by removing the need for nonlinear constraints and by decreasing the number of decision variables to just one third of its original value, significantly reducing solving time (∼27 times faster than the original formulation). Additionally, new expressions derived for the first and second moments of both reservoir release deficit and surplus variables and the already-derived expression for the second moments of reservoir storages are incorporated into the FP-2022 formulation enabling the method to reach an improved optimality for a nonlinear objective function. The enhanced procedure is applied to solving a reservoir operation optimization problem for a major dam in Brazil. The result comparisons made with other methods along with a thorough analysis of release operation policies prove the optimality of this highly numerically efficient and convenient-to-use FP-2022 method. Finally, a multi-reservoir application of the model is also tested with corrective simulations for improved estimates of some additional variables of interest. A specific constraint-handling approach regarding reservoir release lower and upper bounds is also presented. Satisfactory results are obtained for solving the Parambikulam-Aliyar reservoir system, a real world five-reservoir operation optimization problem from India.