Optimal Operation Model Research Articles

Coordinated Optimal Operation Method for Snow-Shaped Distribution Networks Based on Game Theory

The snow-shaped distribution network is developed and upgraded from the traditional single-loop network and double-loop network structure. It is composed of three or four groups of single-loop networks in pairs that form a feeder cluster in the form of a ring network. Based on this topology, the coordinated optimization operation problem is studied. First, the game elements are analyzed, and then a cooperative game-based, coordinated optimization operation model is established by taking the minimum network loss and voltage deviation of the system as the utility function. Then, the alternating direction multiplier method (ADMM) is employed in Cplex solver to solve the established optimization model. Finally, a numerical case is given to verify the effectiveness of the proposed model and method. The results show that, by using the complementary difference relationship of source–charge characteristics between different regions in the snow-shaped distribution networks, the cooperative game model can realize the energy interconnection among different regions to improve the operation level.

Research on Status Assessment and Operation and Maintenance of Electric Vehicle DC Charging Stations Based on XGboost

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards.

Machine learning-based rainfall forecasting in real-time optimal operation of urban drainage systems

This paper presents a rainfall forecast-based real-time optimal operation model of urban drainage systems (UDSs) investigating the role of rainfall forecasts in predictive real-time operation of UDSs. Two rainfall forecast models, namely long short-term memory (LSTM) and optimized-by-PSO (particle swarm optimization) support vector regression, are linked to an integrated simulation–optimization model. For any given operation policies, the rainfall-runoff stormwater management model (SWMM) simulates the urban system’s operation under flooding conditions in which the operation policies are optimized by the metaheuristic harmony search (HS) optimizer. The objective function of the HS algorithm is minimizing flood volumes at control points. The presented approach is therefore a dynamic predictive real-time operation (PRTOP) model that continuously updates rainfall forecasts and operational decisions during flood events. The performance of the developed forecast-based integrated SWMM-HS simulation–optimization modeling framework is tested through its application in a real urban system case study located in Tehran, Iran. Results demonstrate that a notable reduction in flood volume (11.8%) is achieved by using the LSTM-driven rainfall forecasts in the proposed PRTOP model compared to a reactive real-time operation (RTOP) model not utilizing rainfall forecasts.

Including dynamic capacity impact in an improved model for optimal flood control operation in river-type reservoirs

Flood control operations in river-type reservoirs are significantly affected by both the dynamic reservoir capacity and flood propagation within the reservoir area. However, the traditional reservoir flood control optimal operation (RFCOO) model is usually based on the static capacity method, which may bring large errors in scheduling calculations for river-type reservoirs. To address this issue, an improved model, the reservoir dynamic capacity flood control optimal operation (RDCFCOO) model, that includes the influence of the dynamic reservoir capacity and flood propagation, was developed to improve the accuracy of flood regulation calculations in river-type reservoirs. This improved model includes a 1D unsteady flow simulation and a rederived objective function based on the maximum peak clipping criterion, and expresses the state transformation equations using the de Saint-Venant equations. Furthermore, a multi-core parallel DP (PDP) algorithm that incorporates reduction of state dimensionality (RSD) was developed to effectively derive optimal operation schemes. The improved model and algorithm developed herein were validated through an application to the Xiangjiaba Reservoir, China. The results show that: (1) By adding the item (qiw) to the objective function and substituting the de Saint-Venant equations for the water balance equation, the RDCFCOO model developed in this paper can account for the impact of dynamic capacity and flood propagation. As a result, it exhibits a more accurate and detailed flood control process that was closer to the actual conditions of river-type reservoirs compared to the RFCOO model; (2) By removing the invalid discrete state points from the search space and fully utilizing multi-core processors, the proposed PDP with RSD can significantly improve the computational efficiency of the DP in solving RDCFCOO problems. The improved model and algorithm can effectively improve the calculation accuracy of flood control optimal scheduling in river-type reservoirs and provide more information for decision-makers.

Optimal operation of multi-energy microgrids with shared energy storage clusters by master-slave games

Abstract As shared energy storage power plants have become a research hotspot, the cooperation between them and microgrids has attracted attention. In this paper, we propose a master-slave game optimization operation strategy for multi-energy microgrids, taking into account shared energy storage (ESS) clusters. Firstly, the inclusion of ESS clusters is considered in a combined heat and power (CHP) type multi-energy microgrid. Then, the optimal operation model is established and solved in two layers. The master-slave game model in the context of ESS clusters is proposed by analyzing the game relationship between the microgrid side and the user side, the arithmetic simulation with MATLAB, and the modeling and solving are carried out by the Yalmip tool and the CPLEX solver, and the combination of heuristic algorithms and the solver is used to optimize the strategies of the microgrid side and the user side. The optimization of the system after the optimization of this paper’s methodology can enhance the system economy.

Differential Pressure Power Generation in UGS: Operational Optimization Model and Its Implications for Carbon Emission Reduction

Underground Gas Storage (UGS) is an important facility for natural gas peak adjustment and ensuring the balance between energy supply and demand. The daily gas injection and production capacity of large UGS can reach several hundred million or even billions of cubic meters, and the energy consumed by the injection and production equipment is not to be underestimated, which also contains a large amount of pressure energy that can be utilized for power generation. In this context, the optimization of low-carbon operation of UGS is of great practical significance in engineering. In this study, an innovative UGS system operation optimization method is proposed to integrate a natural gas differential pressure generator set in the UGS system. Considering the complex interconnections among multiple storage areas, multiple wells and wellsites, and the hydro-thermodynamic constraints during the operation of injection and production pipeline network, and with the objective of minimizing the carbon emission, the UGS integrated with Differential Pressure Power Generation System (UGSIDPPGS) is established. In order to solve this complex model, this paper designs an optimization solution framework based on variational assignment, which effectively avoids the problem of falling into inferior solutions during the solution process. The optimization model is applied to the case study of a large-scale depleted gas reservoir in China, and the optimal operation schemes under three scenarios are successfully obtained. The results show that the UGSIDPPGS optimization model, not only effectively utilizes the pressure energy, generates up to 56.908×106kW·h per month, and reduces the CO2 emission by 56,738 tons, but also achieves the low-carbon and economic operation of the UGS. In conclusion, the operation strategy proposed in this study is of great value for optimizing the operation of UGS, and provides practical guidance and suggestions for the low carbon and environmental protection of UGS and the utilization of new energy sources.

Application of intelligent algorithms in optimal operation of pumping stations

Abstract An optimal operation model of pumping stations is established in this paper, with the minimum energy consumption of the pumping station as the optimization objective, considering the constraints of the pumping station operating head, flowrate of the pumping station and so on. Simulated annealing is introduced into genetic algorithm, particle swarm optimization, wolf pack algorithm and grey wolf optimization to solve the model. The minimum value, average value, relative standard deviation of the objective function and infeasibility of constraint conditions are selected as the evaluation indexes of the algorithms performance. The calculation results show that simulated annealing ensures that the intelligent algorithms can find the optimal solution in the feasible solution space and avoid them falling into local optimization. The operation schemes obtained by solving the example model with the improved algorithm can reduce the energy consumption of the pumping station by 5.759%∼6.682%, which provides guidance for the operation schemes of the example pumping station. The results can be further applied in other pumping stations.

Optimal operation model of ecological flow of hydropower station based on genetic algorithm and neural network

Amid diverse water demands, the conflict between ecological flow adjustments and power generation flow adjustments in hydropower station water resource dispatching, coupled with an increasing diversity of constraints, complicates the issue of water resource optimization and dispatching. How to scientifically develop feedback regulation mechanisms to promote a virtuous ecological cycle, fine tune existing hydropower station scheduling plans, and achieve multi-objective optimization scheduling is the core issue in researching the comprehensive utilization of water resources. This study establishes a reservoir flow regulation model with the objective functions of maximizing power generation and achieving optimal average ecological protection. The model, constrained by water balance, reservoir capacity, unit output, and unit overflow, was optimized and solved using the genetic algorithm-backpropagation neural network (GA-BPNN) approach. This study establishes a reservoir flow regulation model with the GA-BPNN algorithm was applied in multi-objective optimization to identify an optimal solution that maximizes overall system performance while adhering to ecological flow constraints. The results indicate that the optimized plan has performed effectively in hydropower station operations. It not only satisfies ecological flow requirements, but also significantly enhances power generation efficiency. Specifically, power generation increased by 32,680 kw·h, a 23.85% growth rate. The optimized plan significantly enhances the comprehensive utilization efficiency of water resources. It offers a scientific basis for the rational planning, dispatching and utilization of water resources in hydropower stations and reservoirs.

Flexibility-Oriented AC/DC Hybrid Grid Optimization Using Distributionally Robust Chance-Constrained Method

With the increasing integration of stochastic sources and loads, ensuring the flexibility of AC/DC hybrid distribution networks has become a pressing challenge. This paper aims to enhance the operational flexibility of AC/DC hybrid distribution networks by proposing a flexibility-oriented optimization framework that addresses the growing uncertainties. Notably, a comprehensive evaluation method for operational flexibility assessment is first established. Based on this, this paper further proposes a flexibility-oriented operation optimization model using the distributionally robust chance-constrained (DRCC) method. A customized solution method utilizing second-order cone relaxation and sample average approximation (SAA) is also introduced. The results of case studies indicate that the flexibility of AC/DC hybrid distribution networks is enhanced through sharing energy storage among multiple feeders, adaptive reactive power regulation using soft open points (SOPs) and static var compensators (SVCs), and power transfer between feeders via SOPs.

Multi-objective robust optimization for optimal operation of gas-electricity networks with the penetration of integrated P2G and electric vehicle parking lots

Energy is considered one of the most important human needs. Therefore, various energy resources are exploited to meet human needs. Among all available resources, electricity and natural gas have accounted for the largest share in meeting these needs. Accordingly, management of these resources is one of the most important issues in different societies. This paper presents a multi-objective optimization model for optimal management and operation of the power distribution network and natural gas network as well as reducing carbon emissions. In this modeling, the natural gas network and distribution power network are connected through two electric vehicle parking lots (EVPLs) and two gas-fired units (GFUs), and these two EVPLs and GFUs exchange energy with each other. The EVPLs are equipped with various facilities including power-to-gas (P2G) systems and natural gas storage that facilitate energy exchange between the two networks. The modeling presented in this article is based on mixed integer linear programming, so the nonlinear equations including power flow equations, the Weymouth equation and the cost functions of GFUs are also linearized using various methods. It should be noted that the price of purchased electricity poses uncertainty which is modeled using robust optimization techniques. The proposed model is tested on the 33-bus power distribution network and the 20-node natural gas network. The results show that the presence of EVPLs significantly reduces the operational costs of the natural gas network and power distribution network and carbon emissions.

Research on cooperative operation optimization method of internet data center alliance considering diversified flexibility

With the rapid development of large-scale internet data center in China, the goal of carbon peak and carbon neutrality has put forward higher requirements for low-carbon economic operation of internet data center. Considering complementary advantages among internet data centers, this paper proposes a cooperative operation optimization method of internet data center alliance based on diversified flexibility. Firstly, a cooperative operation structure of alliance considering electricity and carbon trading between members was built, as well as the diversified flexibility model considering carbon capture and power to gas device. Then, a cooperative optimization operation model of alliance was constructed based on Nash bargaining theory, and the allocation of cooperation benefit among members can be realized based on electrical carbon contribution rate. In order to improve solution convergence, the alternating direction multiplier method is improved by penalty factor updating and correction factor modeling method. Finally, the proposed method is applied to the demonstration project of “East Data and West Computing” in Qingyang, Gansu, China, and the trial run results are consistent with the theory analysis. On the one hand, the diversified flexibility and the electricity and carbon trading between members can effectively reduce the total cost and carbon emissions of alliance. On the other hand, the cooperation benefits distribution strategy based on the electrical carbon contribution rate can effectively enhance enthusiasm of members to participate in alliance. The improved alternating direction multiplier method can achieve distributed optimization of alliance, protect the privacy of each member, and has good convergence and scalability.

Optimizing sustainable energy integration: A novel approach using concentrated solar plant and hybrid power supply

This study pioneers sustainable energy solutions amidst escalating demand and fossil fuel depletion. Its primary novelty lies in proposing an integrated energy system, encompassing a concentrated solar plant, thermal energy system, and hybrid power supply within the solar energy domain. This integration substantially enhances overall system performance. The architectural design integrates various energy conversion components, including wind turbines, energy storage devices, and electric heaters, fostering synergistic collaboration. The optimization of energy usage is a key focus, utilizing heating and electricity costs as pivotal signals for participation in an Integrated Demand Response program (IRD). The demand response model, encompassing both thermal and electric loads, incorporates a sophisticated price elasticity matrix. Addressing challenges associated with renewable energy intermittency, phased carbon emissions, and the advantages of demand response, this study introduces an advanced system operation optimization model to effectively curtail operational costs. In the realm of methodology, this research breaks new ground by proposing an enhanced optimization algorithm that ingeniously combines the Whale Optimization Algorithm (WOA) with the Wavelet Transform (WT). This novel amalgamation significantly enhances the algorithm's search capabilities, providing superior performance in tackling the complexities inherent in the optimization problem. The proposed model undergoes rigorous evaluation in a comprehensive study featuring diverse energy sources and multiple scenarios. The outcomes of this evaluation distinctly showcase substantial reductions in overall operational costs compared to conventional approaches. This multifaceted contribution positions the study at the forefront of endeavors to revolutionize energy systems for enhanced sustainability and efficiency.

Two-Stage Robust Optimization of Integrated Energy Systems Considering Uncertainty in Carbon Source Load

Integrated Energy Systems (IESs) interconnect various energy networks to achieve coordinated planning and optimized operation among heterogeneous energy subsystems, making them a hot topic in current energy research. However, with the high integration of renewable energy sources, their fluctuation characteristics introduce uncertainties to the entire system, including the corresponding indirect carbon emissions from electricity. To address these issues, this paper constructs a two-stage, three-layer robust optimization operation model for IESs from day-ahead to intra-day. The model analyzes the uncertainties in carbon emission intensity at grid-connected nodes, as well as the uncertainty characteristics of photovoltaic, wind turbine, and cooling, heating, and electricity loads, expressed using polyhedral uncertainty sets. It standardizes the modeling of internal equipment in the IES, introduces carbon emission trading mechanisms, and constructs a low-carbon economic model, transforming the objective function and constraints into a compact form. The column-and-constraint generation algorithm is applied to transform the three-layer model into a single-layer main problem and a two-layer subproblem for iterative solution. The Karush–Kuhn–Tucker (KKT) condition is used to convert the two-layer subproblem into a linear programming model. A case study conducted on a park shows that while the introduction of uncertainty optimization increases system costs and carbon emissions compared to deterministic optimization, the scheduling strategy is more stable, significantly reducing the impact of uncertainties on the system. Moreover, the proposed strategy reduces total costs by 5.03% and carbon emissions by 1.25% compared to scenarios considering only source load uncertainty, fully verifying that the proposed method improves the economic and low-carbon performance of the system.

Transit Dynamic Operation Optimization Using Combination of Stop-Skipping Strategy and Local Route Optimization

The efficient operation and intelligent upgrading of public transportation can effectively enhance the attractiveness of conventional public transportation. In order to improve the delicacy management level of bus operations, this study designed a new dynamic optimization model for single-line bus operations with the dual optimization objectives of the lowest passenger travel cost and lowest operation cost, using a combination of the strategy of stop-skipping control and local route optimization. Simulated annealing (SA) was introduced into the genetic algorithm (GA) to design a hybrid heuristic algorithm for model solving. The effectiveness of the optimization model and the hybrid algorithm were verified and evaluated by using the No. 115 bus line in Ganzhou City as an example. The results showed that the proposed optimization model had a good usability, which can effectively improve the average vehicle speed, shorten the overall waiting time of passengers, and enhance the operational efficiency of the line. The hybrid algorithm saw significant improvement in terms of the iteration speed and the quality of the optimal solution compared with the conventional genetic algorithm.

Asymmetric Nash bargaining model for operation optimization of multi-integrated energy systems considering peer-to-peer energy trading

Energy interactions across integrated energy systems constitute crucial means to enhance energy efficiency and match supply and demand. However, the cooperative operation and benefit distribution among multiple integrated energy systems still need in-depth exploration. To fill this gap, this paper presents an asymmetric Nash bargaining optimization model for multiple integrated energy systems considering peer-to-peer trading. First, a scheduling model for multi-integrated energy systems is formulated considering carbon trading. Then, the model is incorporated into Nash bargaining framework and transformed into the alliance cost minimization subproblem and peer-to-peer trading payment bargaining subproblem. The bargaining power factor is introduced to measure the contribution of participants in energy sharing. The alternating direction multiplier method is utilized to handle the proposed model. Finally, a case study is carried out to validate the validity of the proposed strategy. The results show that compared with the independent operation mode, the performances of three integrated energy systems in collaborative operation mode are enhanced by 11.7 %, 9.0 %, and 4.8 % respectively. The distributed algorithm can reduce the computation time by 30 % and obtain highly efficient solutions while protecting private information of each participant. This research provides support and practical tools for conducting peer-to-peer transactions of multiple integrated energy systems.

Multi frequency stability optimization of integrated energy systems considering virtual energy storage characteristics of heating networks

With the development of market-oriented reformation, integrated energy system with high proportion of new energy installed capacity are gradually facing the pressure of power fluctuation stabilizing. This paper exploits the weak equilibrium characteristic of thermal system to solve high frequency fluctuation of new energy sources. It establishes a multi-frequency stable operation optimization model of integrated energy system considering the virtual energy storage characteristics of thermal network. Firstly, the model exploits the wavelet packet decomposition method for the frequency decomposition of the fluctuation stabilizing demand faced by the integrated energy system. Secondly, this study investigates the technical characteristics of source-load-storage equipment regulation and heat network temperature-quantity regulation in the thermal system. Finally, considering the equipment life and system fluctuation smoothing needs, an integrated energy system stability optimization model is established. Meanwhile, a modified honey badger algorithm is proposed to realize the case optimization simulation.The result shows that the total operating cost of the system is reduced by 8.45%. As the thermal system regulation replaces the high-frequency regulation function of the energy storage equipment, the service life of battery increased by 67.6%. Therefore, the model effectively solves the integrated energy system fluctuation stability problem with high proportion of new energy installed capacity.

Enhancing Distribution Networks with Optimal BESS Sitting and Operation: A Weekly Horizon Optimization Approach

The optimal sitting and operation of Battery Energy Storage Systems (BESS) plays a key role in energy transition and sustainability. This paper presents an optimization framework based on a Multi-period Optimal Power Flow (MOPF) for the optimal sitting and operation of BESS alongside PV in active distribution grids. The model was implemented in AMPL (A Mathematical Programming Language) and solved using the Knitro solver to minimize power losses over one week, divided into hourly intervals. To demonstrate the applicability of the proposed model, various analyses were conducted on a benchmark 33-bus distribution network considering 1, 2 and 3 BESS. Along with the reduction in power losses of up to 17.95%, 26% and 29%, respectively. In all cases, there was an improvement in the voltage profile and a more uniform generation curve at the substation. An additional study showed that operating over a one-week horizon results in an energy gain of 1.08 MWh per day compared to single daily operations. The findings suggest that the proposed model for optimal sitting and operation of BESS in the presence of Renewable Energy Sources (RES) applies to real-world scenarios.

A two-stage optimal operation strategy for community integrated energy system considering dynamic and static energy characteristics

The community integrated energy system (CIES) can provide users with reliable and economical energy supply through centralized utilization of various energy equipment. However, due to the significant difference in inertia between different energy sources, the response speed of equipment using different energy sources varies greatly, making it difficult to cooperate well and limiting their ability to cope with source-load uncertainty. Based on this consideration, a two-stage optimal operation strategy for CIES is proposed to utilize dynamic and static energy separately. First, the dynamic and static energy are defined and divided, and their different functions are analyzed. Based on this, a two-stage optimal operation architecture is proposed. Then, a day-ahead interval optimal operation model considering dynamic and static energy matching is established. In which, the uncertainty of source and load is characterized by nonlinear interval numbers, and the interval possibility degree is used for deterministic transformation of nonlinear interval numbers. Thirdly, a dynamic and static energy replacement mechanism based on model predictive control is given, and an intra-day rolling optimal operation model considering dynamic and static energy replacement is established. Finally, a CIES is used as an example to demonstrate the rationality and effectiveness of the proposed strategy. Simulation results show that the proposed strategy can effectively utilize different types of energy equipment and significantly reduce the adverse effects of source-load uncertainty.

Collaborative operation strategy of multiple microenergy grids considering demand‐side energy‐sharing behavior

AbstractIn recent years, as the energy market continues to change, an emerging business model is gradually emerging as an energy provider (EP). The participation of EP as an independent entity in the distribution network investment model not only helps realize a win–win situation for multiple stakeholders but also promotes the innovation and sustainable development of the energy industry. However, in the energy system, EPs, grids, microenergy grids (MEGs), and shared energy storage service providers usually belong to different stakeholders, and their respective pursuit of profit maximization can easily lead to uncontrolled competition, which greatly reduces market efficiency. To address the varying ownership structures within the energy system, a refined bilevel coordinated optimization operation model is introduced, rooted in the master–slave game theory. In this model, the EP assumes the role of the master, while the MEG operator and the shared energy storage operator (SESO) act as slaves. A peer‐to‐peer energy‐sharing mechanism between MEGs and an energy trading mechanism between MEGs and shared energy storage is used to further increase the local consumption rate of new energy. Simulation results demonstrate that the proposed method not only boosts the revenues of an EP, SESO, and MEG operators but also effectively accounts for the MEG's energy storage needs and SESOs' profitability.

Optimal planning of integrated electricity-natural gas distribution systems with hydrogen enriched compressed natural gas operation

In future integrated electricity-natural gas distribution systems (IEGDS), hydrogen and methane converted from excessive renewable generation will be mixed with natural gas to form hydrogen enriched compressed natural gas (HCNG) for fewer carbon emissions. Consequently, optimal planning of the HCNG-integrated IEGDS plays an essential role in increasing the economy and technical benefits. However, integrating HCNG operation into the planning of IEGDS still has many challenges, such as unrealistic modeling, nonlinear constraints, and the potential risk of voltage violations. The purpose of this paper is to develop a tractable optimization model for planning and operation of the HCNG-integrated IEGDS, aiming to minimize the investment and operating cost, reduce the voltage violation, and improve the renewable power penetration rate. To achieve this, a bilevel optimization model is developed for optimal planning of wind turbines and soft open points (SOPs) in the IEGDS considering comprehensive HCNG operation, in which both hydrogen and methane injection are considered and optimized for different system operation scenarios. Furthermore, the proposed nonlinear model is reformulated to a tractable bilevel mixed-integer second-order cone model using several reformulation techniques and solved by the reformulation and decomposition algorithm. Case studies, performed using the publicly available datasets, are conducted to demonstrate the economic and technical improvement by implementing the proposed planning model. The annual planning results show that incorporating the coordination between SOPs and methane injection results in a 6.77% reduction in investment cost, a 22.64% reduction in operating cost, a 62.69% decrease in voltage violation, and a 23.58% increase in the wind power penetration rate. Moreover, SOPs are more applicable to the safe operation of the IEGDS under high energy load conditions.