Optimal Operation Model Research Articles

RETRACTED:Optimum planning of energy hub with participation in electricity market and heat markets and application of integrated load response program with improved particle swarm algorithm

The drive for improved energy efficiency and flexibility has birthed an innovative solution: an energy hub. This hub operates in sync with natural gas and electricity grids, integrating batteries and thermal storage to optimize renewable energy use, reduce costs, and bolster network stability. In addressing these objectives, a mathematical optimization model for energy hub operation is developed in this paper. This model captures the intricate interplays between electricity and natural gas networks, accounting for the intricacies posed by uncertainties and variations in renewable energy sources and demand profiles. Also, this paper uniquely delves into the integration of load response programs, an approach geared towards amplifying the energy hub system's flexibility and reliability. Through these programs, subscribers gain the agency to calibrate their energy consumption based on real-time pricing signals and incentives dispensed by the energy hub. The simulation findings serve as a compelling testament to the efficacy of the proposed system. Operating costs witness a substantial reduction, the adoption of renewable energy sources is markedly heightened, and the stability and reliability of the upstream energy networks are tangibly ameliorated. The integration of load response programs emerges as a particularly effective strategy in mitigating peak demand and augmenting the system's overall energy efficiency. Underpinning the entirety of this innovative solution is the transformation of the challenges into an optimization problem. This catalyzes the employment of a newly developed particle community algorithm, custom-tailored to surmount the challenges of local optima. This algorithm inherently bolsters the system's search capabilities, minimizing the risk of entrapment within localized solutions. Ultimately, the proposed energy hub system encapsulates a promising trajectory toward the realization of a sustainable and adaptable energy landscape. By seamlessly integrating diverse energy sources and storage systems, augmenting energy flexibility and efficiency, and aligning with broader energy transition and decarbonization goals, this solution presents a formidable contender. The simulation outcomes underscore its potential by showcasing a substantial 4.57 % reduction in the cost of procuring energy from the upstream electrical network.

Optimizing pumped-storage power station operation for boosting power grid absorbability to renewable energy

Optimizing peak-shaving and valley-filling (PS-VF) operation of a pumped-storage power (PSP) station has far-reaching influences on the synergies of hydropower output, power benefit, and carbon dioxide (CO2) emission reduction. However, it is a great challenge, especially considering hydro-wind-photovoltaic-biomass power inputs. This study proposed a novel optimization operation framework for a PSP station driven by the PS-VF operation for boosting power grid absorbability to renewable energy inputs. An optimization operation model based on a grasshopper optimization algorithm was developed to minimize the residual load volatility. A PSP station in the Hunan Province of China constituted the case study, and the practical operation scheme formed the benchmark. The findings underscore the effectiveness of the proposed approach in fostering remarkable synergy, evident in substantial improvement rates of 61% for power output, 58% for power benefit, and 62% for CO2 emission reduction compared to the practical operational scheme. This study not only furnishes valuable scientific and technical backing for the PS-VF operation of the PSP station, enhancing the interplay between hydropower generation, financial benefits, and CO2 reduction, but also provides policymakers with well-informed strategies that consider potential load volatilities and advantages. These strategies are geared towards enhancing the power grid's capacity to assimilate hydro-wind-photovoltaic-biomass power inputs, aligning with the goals of sustainable renewable energy development.

Multiobjective Operation Optimization of Wastewater Treatment Process Based on Reinforcement Self-Learning and Knowledge Guidance.

This article proposes a multiobjective operation optimization method based on reinforcement self-learning and knowledge guidance for quality assurance and consumption reduction of wastewater treatment process (WWTP) with nonstationary time-varying dynamics. First, operation optimization models are developed by online sequential random vector functional-link (OS-RVFL) neural network, which can realize online sequential learning of model parameters. Then, a knowledge base is established to store typical optimization cases for knowledge guiding the subsequent optimizations. Based on it, a reinforcement self-learning-based multiobjective particle swarm optimization (RSL-MOPSO) algorithm is proposed to perform optimization calculation. In this algorithm, reinforcement self-learning is used for interaction learning between environment and action in optimization, and the particle motion trend of algorithm is adjusted according to the feedback information of the optimization process. The effects of wastewater state parameters on particles are recorded and reused to improve the solution quality and calculation efficiency of optimization. Moreover, to make good use of the information of the previous optimizations and balance the coordination between global search in the early stage and local search in the later stage, a selective information feedback mechanism is further proposed to ensure the diversity and convergence of the algorithm. Finally, prediction-based intelligent decision making is performed to select the final optimization solution as the final setpoints for the lower-level controllers from the Pareto frontier with considering specific technical requirements. Data experiments show that the proposed method can effectively reduce energy consumption and ensure effluent quality.

Operation Optimization of Biomass Integrated Energy System Based on Adjustable Heat-to-Electric Ratio

Considering the application of biomass energy, the Biomass Integrated Energy System (BIES) was first constructed. An integrated energy system operation optimization model was proposed with the objective functions of minimizing economic costs and maximizing clean energy utilization. Secondly, according to the characteristics of biomass Cogeneration, the scheme of adjusting the ratio of heat and power is proposed. Finally, a simulation analysis was conducted on a certain region in China. The results indicate that utilizing biomass energy in an integrated energy system can greatly reduce operating costs and improve energy utilization efficiency. After the heat-to-power ratio is adjusted, economic costs can be reduced again by 7.66%, and clean energy utilization can be increased by 6.15%.

Optimal operation of microgrid with consideration of V2G's uncertainty

AbstractThe aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs’ behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs’ V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA.

Operation optimization of regional integrated energy systems

AbstractTo study the role of energy system demand‐side regulation capacity on carbon emission reduction, we proposed a regional integrated energy system (RIES) optimal dispatch model considering carbon trading cost and dynamic demand response (DR). First, based on the framework of the RIES, the impact of DR of transferable load, reducible load, and substitutable load demand on system operation, considering the response characteristics of electrical and thermal load, was analyzed. Second, the carbon quota of the system was analyzed, and the effect of carbon emissions and carbon capture technology on system operation was considered. Finally, a low carbon optimized operation model of RIES was established with system operating cost and operating efficiency as the target, and system capacity and energy storage power as the constraints. The effectiveness of the optimized operation of the improved intelligent evolutionary algorithm nondominated sorting genetic algorithm II (NSGA‐II) was verified as a research method. The results show that the energy efficiency of the system can be improved by tuning the DR of load demand with the application of the optimal operation model, and a coordination optimization on the economic and low carbon in RIES operation can also be achieved by the improved NSGA‐II algorithm.

Research on Optimal Operation of Substation Self-consumption Load Considering Uncertainty

Under the goal of "double carbon", in order to reduce the power consumption, reduce the cost of carbon emission and increase the utilization rate of renewable energy, we propose the optimal operation method of substation self-powered load considering uncertainty. The optimal operation model of substation is established with the objective function of minimizing the operating cost, carbon emission cost and load power variance. The optimal operation strategy of the substation is solved by the affiliation function and particle swarm algorithm, and at the same time, the starting and stopping situations of each load of the substation are solved according to integer planning. A substation in Henan Province is analyzed as an example, and the results show that the method has obvious advantages in reducing the cost of electricity consumption, carbon emission cost, and peak-to-valley difference of the power grid when considering different objectives for optimization comparison under the consideration of photovoltaic and time-sharing tariffs.

Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms

Quantitative analysis of the factors affecting the energy consumption of metro trains and finding out the breakthrough points of energy conservation is of great practical significance for reducing transport costs and improving energy utilization. Train operation curve optimization is one of the main methods to reduce the energy consumption of train operation. This paper introduces the metro train energy-saving operation optimization model with fixed travel time. Firstly, taking the energy consumption index and punctuality index as the objectives, a train energy-saving operation optimization model is established. Then, the solution method based on the improved NSGA-II algorithm is developed. Finally, the developed optimization model is applied to Guangzhou Metro Line 7 to verify the performance. The results show that the energy consumption of the developed operation strategy is 24.4 % less than that of the minimum travel time strategy, and the total operation time meets the punctuality requirements. Meanwhile, the improved NSGA-II algorithm has less energy consumption than GA and PSO algorithms in the application of Guangzhou Metro Line 7.

Multi-Dimensional Collaborative Operation Model and Evaluation of Cascade Reservoirs in the Middle Reaches of the Yellow River

Reservoir operation optimization is a technical measure for flood control and is beneficial owing to its reasonable and reliable control and application of existing water conservancy and hydropower hubs, while ensuring dam safety and flood control, as well as the normal operation of power supply and water supply. Considering the beneficial functions of reservoirs, namely flood control and ecological protection, this paper firstly established a two-objective optimal operation model for the reservoir group in the middle reaches of the Yellow River. We aim to maximize the average output of the cascade reservoir group and minimize the average change in ecological flow during the operation period under efficient sediment transport conditions, with the coordination degree of water and sediment as the constraints of reservoir discharge flows. The paper aims to construct an evaluation index system for reservoir operation schemes, apply a combined approach of objective and subjective evaluations, and introduce the gray target and cumulative prospect theories. By uniformly quantifying the established scheme evaluation index system, screening the reservoir operation schemes with the fuzzy evaluation method, and selecting the recommended scheme for each typical year, this paper provides a new scientific formulation of the operation schemes of reservoirs in the middle reaches of the Yellow River. The selected schemes are compared with actual data, demonstrating the effectiveness of joint reservoir operation and for multidimensional benefits in terms of power generation, ecology, and flood control.

Role of demand response in the decarbonisation of China's power system

Realising China's carbon peaking and neutrality commitments requires a fundamental transformation to a renewable-dominant power system, presenting new challenges to the balance between power supply and demand. In addition to enhancing flexibility on the supply side, it is necessary to fully exploit the flexibility resources on the demand side. Accordingly, flexible load resources using a demand response (DR) mechanism are expected to play a crucial role. In this study, a DR module was developed for an optimal capacity expansion and operation model, the China renewable energy planning and operation (REPO) model. Three different DR behaviours were modelled, namely load shedding, short-term load shifting, and long-term load shifting. Under a forecasted carbon price, DR in eight major industries can achieve an annual cost saving of approximately 16.8 billion yuan and reduce carbon emissions by approximately 106 million tons by 2030. Investment in DR is cost-effective, particularly because the cost saving is nearly 3 times the present value in 2025 and about 4.3 times in 2030. Moreover, DR has a positive effect on reducing the installed capacity demand of coal power and energy storage, which is conducive to increasing the proportion of renewable energy generation and restraining the fluctuation of marginal power generation cost.

Research on nash game model for user side shared energy storage pricing

With the continuous promotion of the energy revolution, the market-oriented reform of electricity has become the first priority in the energy field, and small-scale energy storage devices on the user side have received more and more attention. However, the disorderly management mode of user-side energy storage not only causes a waste of resources, but also brings hidden dangers to the safe operation of the power grid, such as stability, scheduling and operation, power quality and other problems. To address this issue, this paper proposes a user-side shared energy storage pricing strategy based on Nash game. Firstly, an optimal operation model is established for each participant of energy storage operators, users and grid. Secondly, a cooperative game model is established based on Nash equilibrium theory for the participants under the constraints. Finally, the optimal pricing scheme is solved by simulation analysis of the model, and the feasibility of the proposed pricing mechanism is verified. The results show that the optimal pricing scheme can achieve the purpose of peak shaving and resource saving. At the same time, it can also realize the win–win situation for all parties.

Fast energy transition as a best strategy for all? The nash equilibrium of long-term energy planning strategies in coupled power markets

The research links energy system development planning to day-ahead energy markets, market coupling, and renewable energy integration, with a novel approach based on game theory. A two-level method is suggested for long-term energy strategy decisions. In the first stage, four hypothetical zones are simulated using an energy system's operation optimization model, emphasizing electricity flows. Game theory is employed in the second stage to select the best market-coupled zone strategy. A game reflecting transition dynamic, renewables integration, and demand response is formulated in the second step of the approach, where each of the four zones have two possible strategies (fast or slow transition), resulting in 16 sets of strategies (scenarios). Results demonstrate the feasibility of determining a Nash equilibrium, enhancing decision-making compared to prior methods. For the observed hypothetical case, a pure Nash equilibrium is found, where all zones opt for a rapid energy transition.

Integrated model for optimal scheduling and allocation of water resources considering fairness and efficiency: A case study of the Yellow River Basin

Water is an important natural resource with economic and social attributes. The most important aspects of water resource regulations are fairness and efficiency. Achieving a balance between fairness and efficiency is a challenging but popular topic in related research. In this study, three optimal water-resource allocation models were constructed: efficiency priority (E-P), fairness priority (F-P), and stratified water supply (SWS). The three models were applied to the Yellow River Basin (YRB), and the results show that, compared with the E-P and F-P models, the SWS model is a more equilibrium model with significant advantages. Considering the impact of reservoirs on water allocation, an optimal operation model for cascade reservoirs groups, considering fairness and efficiency, was constructed based on the principle of welfare economics and coupled with the SWS model. Proxy modeling was used in the coupling process to enhance the computational efficiency of the integrated model. Compared with the SWS model, the integrated model can better satisfy the water supply demand outside a river while ensuring the ecological environment and sand flushing demand inside the river; its fairness and efficiency are also significantly improved. In addition, integrated models can effectively address hydrological uncertainty and reduce losses. The model proposed in this study has significant flexibility and application potential and can provide a reference for other governments and water-resource management institutions worldwide.

Optimal operation of multiple integrated energy systems based on a hybrid Taguchi‐compact salp swarm algorithm

AbstractWith the rapid development of integrated energy system (IES), it has become a trend to form multiple integrated energy systems (MIESs) in a certain region. However, there is a lack of coordination and cooperation among MIESs. This paper proposes an optimal operation model of MIESs with four cost objectives, which focuses on energy interaction among MIESs. As a non‐linear and large optimization problem, it is difficult for conventional mathematical methods to solve. Hence, this paper proposes a hybrid Taguchi‐compact salp swarm algorithm (TCSSA). The compact technique can save the operation memory of model. Taguchi method can improve the convergence speed and solution accuracy of model. The proposed algorithm is tested on 28 benchmark functions and applied to optimal operation of MIESs. Results demonstrate that: (1) compared with other famous algorithms, TCSSA can provide more efficient execution and better solutions. (2) The optimal operation of MIESs based on TCSSA can achieve the complementary of energy advantages, which effectively reduces the total cost of MIESs (up to 9.67%).

Rethinking environmental flow management strategies in reservoir operations from an integrated water quality perspective

River damming causes appreciable nutrient retention in reservoirs, increasing the risk of eutrophication in reservoir water. Environmental flow (e-flow) management strategies have been extensively studied to direct reservoir release to meet water quantity demands of downstream ecosystems. However, such strategies also impact reservoir nutrient transport and transformation as well as stored and discharged water quality, which has rarely been considered in existing e-flow management. This study combines a reservoir operation optimization model and a coupled hydrodynamic-eutrophication-sediment model to evaluate e-flow management strategies from an integrated water quality perspective. Multiple nutrients, water, and sediment processes are accounted for as well as the quality of both stored and discharged water. Five common e-flow strategies that focus on various ecosystem protection targets were used to generate operation scenarios. Subsequently, the coupled hydrodynamic-eutrophication-sediment model was used to simulate nutrient cycling processes and water quality under each scenario. Modeling results show that e-flow management strategies cause complex effects on reservoir nitrogen, phosphorus, and algal dynamics. In general, reservoir water quality was the worst in the scenario with largest e-flow release, implying that tradeoffs exist between water quantity and quality of reservoir release. Lower e-flow release resulted in reservoir storage level increases and promoted sediment nutrient release, but the effect was offset by the increase in the nutrient dilution capacity of reservoir water. Additionally, the comparison between inflow and discharged water quality showed that the reservoir contributed to reducing river nitrogen loads under low e-flow releases while increasing river phosphorus loads in all scenarios. The findings support e-flow management decision-making to account for both water quantity and quality targets as well as to coordinate reservoir services and ecological protection.

Research on collaborative operation optimization of multi-energy stations in regional integrated energy system considering joint demand response

In recent years, countries around the world have made great efforts to coordinate energy green and low-carbon development and energy supply. In this context, it is of great significance to build energy stations that can greatly absorb renewable energy. The coordinated operation of multi-energy stations in the region can expand the role of energy stations and strengthen energy complementarity between regions. Aiming at the problem of energy interaction and coordinated operation of multi-energy stations in regional integrated energy system, this paper proposes a two-layer scheduling optimization model, which can effectively reduce the system operation cost and carbon emissions. At the same time, considering the joint demand response, it can effectively reduce the peak power supply pressure of the power grid. In addition, in order to ensure the accuracy and efficiency of the solution, this paper improves the traditional NSGA-II algorithm, greatly improving the overall performance of the algorithm. Finally, the simulation analysis is carried out through an example. The results show that the operation optimization model proposed in this paper can effectively reduce the total energy cost of the system, reduce carbon dioxide emissions, and reduce the peak pressure of the power grid. The coordinated operation optimization method is effective and feasible.

Research on multi-energy collaborative operation optimization of integrated energy system considering carbon trading and demand response

Integrated energy systems (IES) have been applied to raise the efficiency of energy utilization and facilitate the sustainable transition of society and energy system. To further explore the multi-energy coupling capacity and carbon reduction potential of the IESs, this study proposes a cooling-heat-electricity-gas collaborative optimization model of IES given ladder carbon trading (LCT) mechanism and multi-energy demand response (MDR). To begin with, the MDR model is developed following the demand characteristics of the system. Given the incentive effect of the LCT on the low-carbon performance of the IESs, the multi-energy coupling low-carbon operation optimization model is proposed. By constructing four application scenarios, the proposed model is practiced in investigating the performance of IES across various market mechanisms. The simulation findings indicate the introduction of the LCT cuts the carbon emissions of the IES by approximately 6.7%, and the MDR mechanism effectively optimizes the energy use curve by regulating user behavior. The combined consideration of the two market mechanisms saves around 9.21% of the IES's operation cost and strengthens the performance of IES towards the green and economic operation. The carbon market requires rational prices to inspire IES. The proposed model offers a significant benchmark for the IES's green performance optimization.

Distributed low-carbon operational optimization model of an integrated energy system based on ladder carbon trading and integrated demand response

ABSTRACT Integrated energy system (IES) has become significant in the global energy revolution because of its unique advantage of promoting clean energy use and improving energy efficiency. However, its low-carbon operational optimization and capacity allocation challenges have sharply increased because, as its scale continuously grows, its inherent strongly coupled multi energy flow and high nonlinearity characteristics have become more prominent. This study proposes a distributed low-carbon operational optimization mechanism for an IES using integrated demand response (IDR) and ladder carbon trading (LCT). First, a low-carbon IES was modeled as a coupled model between energy operators (EO) that contain combined cooling, heating, and power systems (CCHP) and flexible users (FU) based on the Stackelberg game. Second, the operational optimization model was formulated as a source-load synergistic operation scheduling mechanism based on IDR and the LCT. Finally, the strategy of each stakeholder was obtained using a modified distributed algorithm of genetic algorithm nested quadratic programming (GA-QP). The effectiveness of the proposed model was verified through a case study. The economic benefits for each stakeholder improved, as confirmed through the experimental results. The energy cost of the FU decreased by 4.42%, and the revenue from energy sales of the EO increased by 11.9%; renewable energy was prioritized for consumption, with a consumption rate of 100%; the total output of the core devices for energy cascade utilization increased by 15.6%, significantly improving energy supply efficiency; the total carbon emissions decreased by 8.64%; and power and heat loss rates were 0% and 0.00059%, respectively. Highlights With the rapid development of renewable energy, The integrated energy system relies on its inherent operational advantages of high flexibility, high toughness, and multi energy coupling cascade utilization, demonstrates unique advantages in serving as a clean and renewable energy carrier and adapting to the development of distributed energy The energy cost of flexible users has decreased by 4.42%, and the revenue from energy sales of energy operators has increased by 11.9%; Renewable energy is prioritized for consumption, with a consumption rate of 100%; The total output of the core devices for energy cascade utilization has increased by 15.6%, significantly improving energy supply efficiency; The total carbon emissions decreased by 8.64%, both power and heat loss rates are 0%, the error rate of energy supply is 0.00059% Based on the above research, this paper is established a distributed low-carbon operation optimization model for Integrated energy system coupled the EO that contain CCHP and the FU based on Stackelberg game In view of the gradual deepening of the coupling degree of the IES, the increasingly obvious characteristics of multi-agent, the difficulty of centralized scheduling to protect the interests of each independent entity, and the inadequacy of the system’s energy cascade utilization advantages, this paper establishes a bilateral distributed operation optimization model based on Game theory In response to the insufficient utilization of the advantages of multi energy coupling in existing research on IES, insufficient attention paid to the interaction between supply and demand on both sides of the system, and insufficient enthusiasm of users as independent entities to participate in system operation scheduling, this article adopts a integrated demand response as the load side operation scheduling strategy

Optimization Method of Photovoltaic Microgrid Energy Storage System Based on Price-based DR

Extreme events lead to an increasing number of power outages in the distribution network. It is currently the most effective method to restore power supply after distribution network failure to connect distributed photovoltaic to the distribution network in the form of microgrids. However, the randomness of distributed PV output and load is the biggest obstacle limiting its development. Therefore, an optimization method of photovoltaic microgrid energy storage system (ESS) based on price-based demand response (DR) is proposed in this paper. Firstly, based on the influence of the uncertainty of the time of use (TOU) and load on the price-based DR, a price-based DR model is built. Then, taking the minimum difference between photovoltaic output and load demand as the optimization objective, the optimal operation model of price-based DR based on the fuzzy chance constrained program (FCCP) is established, and an optimization model of photovoltaic microgrid ESS based on FCCP is obtained. Finally, the fuzzy chance optimization problem is converted into a deterministic optimization problem through constraint equivalent treatment. The proposed method is verified by numerical simulations.

Virtual power plant optimal operation considering renewable energy uncertainty

An optimal operation model for a virtual power plant (VPP) is developed to promote the decarbonization of electricity as well as to enable the efficient grid integration of distributed power sources and electric vehicles. The model considers the uncertainty of renewable energy and the environmental punishment of gas turbines to achieve coordinated optimization of distributed resources within the VPP. The case demonstrates the utility of the model.