Optimal Operation Model Research Articles

Demand Response Potential Evaluation of Aggregated High-Speed Trains Toward Power System Operation

The integration of a high proportion of intermittent renewable energy sources and the continuous fluctuation of loads in power systems have created an urgent need for flexible reserves. It has been demonstrated both theoretically and experimentally that high-speed trains (HSTs), with booming electricity consumption, can flexibly change their speed to achieve real-time power climbing, reduction, or even feedback from the train to the grid. In this paper, the flexibility of HSTs is investigated from a power system-oriented perspective, and the demand response (DR) potential of HSTs is exploited based on the kinematics and dynamics equations of trains. The lagging power rebound (LPR) effect after the DR period of HSTs is enclosed for the first time, and a duration-capacity-speed-LPR index system is established to quantify the DR performance of HSTs. Considering the operation requirements of both power and railway systems, a novel operation optimization model for aggregated HSTs is proposed to calculate their maximum DR potential. In the proposed model, the LPR of HSTs is constrained to ensure power system security, and the minimum distance limit between HSTs sharing the same railway is also included to ensure railway safety. In addition, an adaptive solution method based on pseudo-spectral is used to solve the optimization model. This approach balances the requirement for calculation speed in the power system with the need for temporal accuracy in the railway system. Finally, the proposed model and technique are verified using a 3-railway 6-HST system.

Impact on traditional hydropower under a multi-energy complementary operation scheme: An illustrative case of a ‘wind–photovoltaic–cascaded hydropower plants’ system

Establishing a wind–photovoltaic (PV)–hydro hybrid system is a new method to utilize wind and PV power, but how multi-energy complementation affects the working modes of cascaded hydropower plants (CHP) has not been fully revealed. In this study, a wind–PV–CHP system was considered as an example and a multi-objective optimal operation model was constructed considering the maximization of both the power generation and the minimum power output. Simulation and comparison results showed that (1) the regulation capability of the hydropower was mainly used for two purposes: one was to drive the early water storage (or water release reduction) of CHP to improve the overall utilization efficiency of water resources, and the other was to drive the hydropower output to cooperate with the non-regulatable wind–PV power output in order to achieve a complementary output; (2) the different minimum outputs determined the regulation capacity for complementing the wind–PV power output, which represents the degree of complementation; and (3) complementation would significantly affect the minimum output and slightly affect the average output of CHP. Overall, this study improves our understanding of wind–PV–hydro complementary operation and can serve as a reference for actual project operation.

A two-phase optimization model for low-sulphur operation of container liners in the context of carbon neutrality

The shipping industry is under significant pressure to reduce carbon emissions, and it is crucial to ensure that current efforts to reduce sulphur dioxide (SO2) emissions do not result in increased carbon dioxide (CO2) emissions. Against the background of carbon neutrality and sulphur emission control area (SECA) policies, this paper proposes a two-phase mathematical model to study the impact of liner low-sulphur compliance operation on carbon emissions. The model addresses four key problems: fleet deployment (phase Ⅰ); compliance option decisions, speed optimization and cargo allocation (phase Ⅱ). To solve these problems, a novel dual population evolutionary algorithm has been designed. The results show that implementing low-sulphur compliance operations for liner companies does not simultaneously reduce both total costs and CO2 emissions, and the rise in the price of low-sulphur oil, the preference for cost decision-making and the expansion of SECAs will increase CO2 emissions; the current sulphur limit policy needs to be adjusted urgently to implement the carbon neutrality goal of the shipping industry; government subsidies can reduce CO2 emissions to some extent, but low-carbon or zero-carbon alternative energy is an inevitable choice for achieving carbon neutrality in the shipping industry.

Operation Optimization of Wind/Battery Storage/Alkaline Electrolyzer System Considering Dynamic Hydrogen Production Efficiency

Hydrogen energy is regarded as a key path to combat climate change and promote sustainable economic and social development. The fluctuation of renewable energy leads to frequent start/stop cycles in hydrogen electrolysis equipment. However, electrochemical energy storage, with its fast response characteristics, helps regulate the power of hydrogen electrolysis, enabling smooth operation. In this study, a multi-objective constrained operation optimization model for a wind/battery storage/alkaline electrolyzer system is constructed. Both profit maximization and power abandonment rate minimization are considered. In addition, some constraints, such as minimum start/stop times, upper and lower power limits, and input fluctuation limits, are also taken into account. Then, the non-dominated sorting genetic algorithm II (NSGA-II) algorithm and the entropy method are used to optimize the operation strategy of the hybrid energy system by considering dynamic hydrogen production efficiency, and through optimization to obtain the best hydrogen production power of the system under the two objectives. The change in dynamic hydrogen production efficiency is mainly related to the change in electrolyzer power, and the system can be better adjusted according to the actual supply of renewable energy to avoid the waste of renewable energy. Our results show that the distribution of Pareto solutions is uniform, which indicates the suitability of the NSGA-II algorithm. In addition, the optimal solution indicates that the battery storage and alkaline electrolyzer can complement each other in operation and achieve the absorption of wind power. The dynamic hydrogen production efficiency can make the electrolyzer operate more efficiently, which paves the way for system optimization. A sensitivity analysis reveals that the profit is sensitive to the price of hydrogen energy.

Energy saving based on a multi-objective optimization model of the tidal pumping station along the coastal area

The operation of tidal pumping station has an energetic impact on energy course of massive water pumping for a long time. Thus, a multi-objective optimal operation model was developed to optimize the operation scheme and reduce the energy consumption of the tidal pumping station. In the model, a day is divided into several sub-periods according to the regulation of water level in tidal reach. The partition of sub-periods, the number and blade angle of operating units in each sub-period are adjusted to minimize daily operation energy consumption. And the less switching times, the better the reliability and durability of the unit, the lower the failure rate. Therefore, the reduction of wearing parts’ service life is quantified for each switching time in the form of cost. A case study is performed with the tidal pumping station of the South-to-North Water Transfer Project in China. In this paper, energy consumption and switching times are quantified as costs respectively, and the result shows that the daily energy consumption could be saved about 2.28%–6.56%, and the switching times reduced at least 60%. Therefore, this paper could provide an effective optimal operation scheme for the tidal pumping station whose operation is affected by tides.

The low-carbon economic operation strategy of virtual power plant under different electricity-gas-heat-carbon multi-market synergy scenarios

Studying the grid integration of renewable energy power generation is crucial for achieving the goal of carbon neutrality since it may have a significant influence on the secure and reliable functioning of the power system. In order to solve the problem of deviation impact caused by renewable energy fluctuations and the optimal scheduling of VPP (Virtual Power Plant), the study divides the internal aggregation unit of the virtual power plant into two parts to model. One part is the source equipment, including wind power generation equipment, gas turbine, gas boiler and waste heat boiler. And the other part is the generalized Energy storage, including electric vehicles, air conditioners and alternative response loads. Ultimately, a generalized energy storage-based virtual power plant operation optimization model is developed under multi-market coordination of electricity-gas-heat-carbon. According to the study’s findings, adding more power-to-gas technology boosts revenue in the carbon trading market by 25.24 percent. The energy market’s revenue is equal to that in the absence of a carbon trading market, and the income of the natural gas market increases by $ 32.96. The income of the carbon trading market is $ 181.51, and the final operating cost is reduced by $ 180.80, a drop of 7.81%. To sum up, the suggested approach may more effectively achieve the best distribution of different energy sources, increase the dependability of VPP operation, and make it more low-carbon.

Exploring optimal joint operating rules for large-scale inter-basin water transfer projects with multiple water sources, diversion routes, and water demand areas

Study region: The Jiangsu Province section of the South-to-North Water Diversion Project (JS-SNWDP) is an essential section of the SNWDP, which includes multiple water sources, diversion routes, and water demand areas. Study focus: Exploring effective operating rules for the JS-SNWDP is critical to improving its long-term operational performance. However, deriving the operating rule for the JS-SNWDP remains a pressing challenge due to the complexity of its water allocation, which requires trade-offs between multiple water sources and diversion routes when supplying and transferring water. Here, we design a new form of the joint operating rules for the JS-SNWDP that includes water-supply rules and water-transfer rules, and establish a multi-objective optimization operation model by simulation-optimization approach to identify the optimal joint operating rules that contribute to the improvement of water supply reliability and the reduction of operation cost. New hydrological insights for the region: The proposed joint operating rule shows obvious advantages over the regular operating rule, with an increase in the water supply rate (2.0%) while the strongly reduced pumping water (24.7%). Meanwhile, compared with the regular operating rule, the joint operating rule enables a more rational use of the runoff regulation capacity of the lakes, which obviously reducing the pumping water from the Yangtze River and the surplus water in the two lakes obviously. The findings of this study are applicable to improve the operational performance of the JS-SNWDP, and provide a reference for exploring the joint operating rules for other IBWTPs with similar structures.

Urban virtual power plant operation optimization with incentive-based demand response

Urban virtual power plant (VPP) shows great potential in alleviating urban power shortage or power supply-demand imbalance. This study proposes a bi-layer optimization model considering incentive-based demand response (IDR) to optimize the operation scheduling of urban VPP. The bi-layer optimization model includes the lower-layer IDR model and the upper-layer urban VPP optimal operation scheduling model. In the lower-layer IDR model, user satisfaction, comfort and preference are considered. Users mainly participate in the scheduling of urban VPP by IDR. The demand response resources obtained by urban VPP through providing incentives for users can effectively alleviate the energy supply pressure. In the upper-layer urban VPP optimal operation scheduling model, the scheduling plans for generating units and trading plans for the electricity wholesale market can be determined. Through upper-layer scheduling, it can not only meet the energy demand of users, but also minimize the urban VPP operation cost. The experimental results show that the proposed bi-layer optimization model for urban VPP operation considering IDR can achieve urban energy resources optimal allocation and support urban energy supply-demand balance, on the premise of ensuring economic benefits.

Multi-scenario optimization model for operation of inter-basin water transfer-supply systems considering cost–benefit relationships

Abstract The optimized operation of an inter-basin water transfer system is a widely used approach to achieving optimized regional water resource allocation. The costs of water transfers vary considerably, depending on the composition of the water source, the target audience and the route and distance of the transfer. This study proposes a multi-scenario optimization model for the operation of inter-basin water transfer-supply systems that balances the cost–benefit relationships. A two-objective optimization model is first established including minimizing the sum of squared water supply deficits and the cost of water transfers. Three water transfer scenarios are developed for different cost–benefit relationships. Based on these scenarios, the established optimization model is further converted into a single-objective optimization model to avoid the direct calculation of complex water transfer costs. The model is finally solved to obtain water transfer schemes. The proposed model is applied to the eastern route of the South-to-North Water Diversion Project in China. The results show that with a contracted diversion of 60% of the Yangtze River, the reliability of the water supply is 78.2% and the water shortage index is 2.8%, which is a compromise between balancing the water shortage index and the volume and cost of water transfer.

Research on Parameter Optimization of the Optimal Schedule Model of Water Resources for the Jiaodong Water Transfer Project Based on the ICCP Model

Establishing an optimal operation model of water resources is a crucial mean to promote the social and economic development in the Jiaodong area, where water resources are seriously deficient. Constraints of the optimal operation model mainly include water balance constraint, discharge capacity constraint, and constraint on the full utilization of operating water. For water transfer projects that have been in operation for decades, the parameters of these constraints, such as Discharge Capacity (DC), Water Conveyance Efficiency (WCE), Evapotranspiration (E), and Water Supply Volume (WSV), have changed from their original design values, which in turn affect the results of the operation model. In order to address the uncertainties caused by corresponding parameters, according to the characteristics of each parameter, an Interval-Chance Constrained Programming (ICCP) model for the Jiaodong Water Transfer Project was proposed. Interval Programming (IP) and Chance-Constrained Programming (CCP) were used to optimize the parameters of constraints involving WCE, DC, E, and WSV. Then, the Sobol method was used to analyze the sensitivity of each parameter to the operating objective function. The results reveal that (1) total water shortage ratio decreased by [14.82%, 17.26%], [14.81%, 17.25%], and [14.82%, 17.26%], respectively, under the incoming water condition of 50%, 75%, and 95%, indicating that ICCP model can adequately consider complex uncertainties and effectively alleviate water shortage; (2) WCE and DC are important parameters for optimal operation model of water resources, and therefore, channels should be regularly maintained to ensure that WCE and DC would not reduce; (3) Decision variables in this study are in the form of intervals, which are more reasonable because they provide more decision-making options to managers.

Exploring a multi-objective optimization operation model of water projects for boosting synergies and water quality improvement in big river systems

Due to excessive nutrient enrichment and rapidly increasing water demand, the occurrence of riverine environment deterioration events such as algal blooms in rivers of China has become more frequent and severe since the 1990s, which has imposed harmful consequences on riverine ecosystems. However, tackling river algal blooms as an important issue of restoring riverine environment is very challenging because the complex interaction mechanisms between the causes are impacted by multiple factors. The contributions of our study consist of: (1) optimizing joint operation of water projects for boosting synergies of water quality and quantity, and hydroelectricity; and (2) preventing algal bloom from perspectives of hydrological and water-quality conditions by regulating water releases of water projects. This study proposed a multi-objective optimization methodology grounded on the Non-dominated Sorting Genetic Algorithm to simultaneously minimize the excess values of algal bloom indicators (water quality, O1), minimize the used reservoir capacity for water supply (water quantity, O2), and maximize the hydropower generation (hydroelectricity, O3). The proposed methodology was applied to several catastrophic algal bloom events that took place between 2017 and 2021 and thirteen water projects in the Hanjiang River of China. The results indicated that the proposed methodology largely stimulated the synergistic benefits of the three objectives by reaching a 36.7% reduction in total nitrogen and phosphorus concentrations, a 33.1% improvement in the remaining reservoir capacity, and a 41.0% improvement in hydropower output, as compared with those of the standard operation policy (SOP). In addition, the optimal water release schemes of water projects would increase the minimum streamflow velocity of downstream algal bloom control stations by 8.6%–9.4%. This study provides a new perspective on water project operation in the environmental improvement in big river systems while boosting multi-objectives synergies to support environmentalists and decision-makers with scientific guidance on sustainable water resources management.

Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation

In this paper, a distributed online prediction optimization algorithm is proposed to optimize the operation of distributed energy resources (DERs) considering multi-period constraints. First, we build a time-varying operation optimization model of DERs in multi-area distribution networks considering the operation constraints among different periods (e.g., the relationship of DERs energy within a certain amount of time). Second, we design an online prediction algorithm to solve the time-varying model in a distributed way. Specifically, it decouples the sensitivities of power flow states among different distribution areas. Based on this, a model is set up to characterize the mapping relationship among power flow states and the increments of DER output powers among different periods, and to predict the power flow states in the future only depending on the local and some aggregated information of each area. Thus, the solution of the time-varying optimization problem is decomposed into solving several subproblems in a distributed way. Finally, the proposed algorithm is verified in a 502-node distribution system. It achieves the optimal operation of DERs with satisfying the constraints of the energy plan formulated to reserve enough energy for the regulation in future periods. Compared with the centralized method, the proposed algorithm achieves similar optimization results with significant reduction of calculation time.

Effectiveness of carbon emissions trading and renewable energy portfolio standards in the Chinese provincial and coupled electricity markets

China has formulated the carbon emissions trading (CET) system and renewable portfolio standards (RPS) to address climate change. Meanwhile, the development of provincial and coupled provincial-interprovincial electricity markets is underway in China. This paper establishes the operation frameworks and optimization models of the above electricity markets. The research results show that: CET or RPS can both increase the penetration of renewable energy and reduce carbon emissions. The effect of different CET parameters in the provincial electricity market is apparent. However, the effects of different RPS parameter changes on the two markets are fixed.

Optimal planning and operation of grid-connected PV/CHP/battery energy system considering demand response and electric vehicles for a multi-residential complex building

Ongoing technological innovations and communication infrastructure have rapidly increased the active participation of energy customers and utilization of renewable energy sources and electric vehicles (EVs) as climate change solutions. As such, the demand response program (DRP) has been developed as an effective tool for better interaction mechanisms and balancing supply and demand. This paper proposes a model for optimal planning and operation of an integrated PV/CHP/battery/gas boiler hybrid grid-connected energy system with the purpose of minimizing lifecycle cost. The model considers EV charging stations (private and shared), incentive-based DRP, and net energy metering mechanism. Different alternatives for system configuration are optimized and analyzed in terms of cost, emission, and resiliency factors. A new representative case study of a multi-residential complex building with electrical and thermal loads is depicted to validate the research methodology. The results show that the full utilization of the building roof with maximum PV capacity and adoption of the DRP initiatives for both summer and winter seasons reduces the electricity import from the grid and enhanced the use of the on-site CHP generator and battery packs. The optimal system with 73.9 kW PV, 105 kW CHP, and 53 kW inverter is of superior performance with reduction percentages of 9 %, 10 %, 45 % and 16 % in the system's net present cost, energy cost, annual utility bill, and CO2 emissions, respectively, compared to the base scenario of CHP/gas boiler/grid. Also, the proposed system also gets significant DRP revenue of 10,060$/yr and zero unmet demands and a negligible number of EVs missed charging sessions despite the grid outages and intermittency of solar resources. Overall, this work can provide an understanding and a way forward to those working on iHES development for complex buildings and a valuable benchmark model that is transferable and applicable with relative ease for other regions.

Development of an Advanced Optimization and Optimal Control Mathematical Model for Energy-Efficient Operations of Renewable Energy

The potential for developing energy-efficient operations for renewable energy systems has increased with the expansion of renewable energy sources. As a result, creating advanced optimization and optimum control mathematical models will be necessary to handle the difficulties related to their integration into the grid. This study develops an enhanced optimization and optimal control model for the energy-efficient operation of renewable energy systems based on photovoltaic (PV) and hydropower. The created model takes into account the economic element while integrating an optimization method to address the energy management issue of PV systems. PV system operation is optimized taking into account a variety of operational restrictions using economic objectives like profit maximization and cost minimization. To further simplify energy balancing and lower risk, a hydropower energy system is also taken into consideration. Furthermore, by forecasting the ideal PV output and hydropower generation for various economic purposes, a dynamic optimum control approach is created to discover the best operational strategy for the systems. Case studies are used to evaluate this sophisticated optimization and optimum control model, and the findings show how effective it is at lowering operating expenses for the operation of renewable energy systems in an energy-efficient manner. Keywords: Advanced Optimization, Optimization Techniques, Control Strategies, Energy Management, Optimal control, Mathematical model, Energy-efficient operations, Renewable energy

Optimal Energy Management of Internet Data Center With Distributed Energy Resources

Currently, an increasing number of Internet data centers (IDCs) are trying to apply distributed energy resources (DERs), such as renewable energy, battery energy storage systems (BESS), and conventional generators (CG). However, uncertain renewable energy presents significant challenges to the safe and stable operation of IDCs. A two-stage optimal operation model based on distributionally robust optimization (DRO) is proposed for an IDC with DERs to lower the total cost of the IDC, including operating cost, carbon emission cost, and re-dispatch cost under the worst-case probability distribution of renewable energy. An ambiguity set is formed by combining norm-1 and norm-inf under the given confidence level to capture the possible probability distributions of uncertain renewable energy. The proposed model can be turned into a tractable mixed-integer linear programming problem and efficiently solved by the column-and-constraint generation algorithm. Experimental results show the benefits of DERs integration and workload dispatch. Comparative analysis further demonstrates the superiority and scalability of the proposed model.

Deriving operating rules for inter-basin water transfer projects incorporating a scenario reduction strategy

Streamflow uncertainty is one of the main challenges in managing reservoir systems. To accurately characterize the streamflow uncertainty in a reservoir operation model, numerous streamflow scenarios should be considered, but this results in a huge computational burden for multi-objective optimization. Here, we propose a methodology for deriving the operating rules for an inter-basin water transfer project (IBWT). The methodology adopts a scenario-reduction strategy to balance accuracy against the computational burden. The proposed method first reduces a large number of streamflow scenarios to several typical scenarios and the corresponding occurrence probabilities using a simultaneous backward reduction method. A multi-objective optimal operation model is then formulated to optimize the expected values of performance indices of the IBWT system under the typical scenarios. Finally, a simulation-based optimization framework is adopted to derive the multi-objective operating rules. To test the method, the Han to Wei IBWT project in northwest China is considered as a case study. The results show that the proposed methodology produces effective reservoir operating rules with a relatively low computational burden. After the initial 1,000 streamflow scenarios have been reduced to 20, the model solution time decreases significantly, from 49.17 h to 1.05 h, while the changes in mean value (0.47%) and standard deviation (0.93%) of these scenarios are less than 1%. Compared with an operation scheme that uses only historical streamflow data, the operation performance is further improved in terms of energy production, energy consumption, and water shortage index when the streamflow uncertainty is considered.

Optimal operation of distribution networks and multiple community energy prosumers based on mixed game theory

Community energy systems are currently transitioning from conventional consumers to prosumers. Under this context, the coordinated management of multiple community energy prosumers (CEPs) is explored this study in accordance with the same distribution network. The aim of this study is to facilitate the coordination of unit operations, demand response, and peer-to-peer (P2P) energy trading among CEP coalitions based on the distribution network operator (DSO) by setting electricity prices. Thus, a mixed game-based optimal operation model of DSO and CEP alliances is developed, integrating Stackelberg and cooperative games. The upper layer aims at maximizing DSO revenue, whereas the lower layer minimizes the operation costs of CEP cooperative alliances while allocating benefits in accordance with the Nash-Harsanyi bargaining theory. The model is solved with a two-stage distributed algorithm that integrates the bisection method and the alternating direction method of multipliers (ADMM). Furthermore, cloud energy storage (CES) is introduced to optimize the economics of CEP. The effectiveness of the proposed method is verified through a case study, demonstrating the collaborative optimal scheduling of multiple CEPs and reasonable benefit distribution. Accordingly, this study is conducive to expediting the effective and economical management of CEPs under distribution networks.

A Bi-Level Optimal Operation Model for Small-Scale Active Distribution Networks Considering the Coupling Fluctuation of Spot Electricity Prices and Renewable Energy Sources

As the penetration rate of variable renewable energy such as wind power increases in the power system, the composition and balance of the system also change gradually. The intermittency of renewable energy poses great stability challenges to the traditional centralized generation and load-oriented transmission and distribution methods. Therefore, the Active Distribution Network Operator (ADNO) with distributed installation at the local level has a good application prospect in the new scenario. However, ADNO needs to improve its operational efficiency based on the types of local generation and storage devices and the nature of the market environment. To address this issue, this paper proposes a forecasting method that considers the coupling fluctuations of spot electricity prices and renewable energy, and a bi-level optimization operation method based on the Stackelberg game for optimizing the operation of small-scale ADNO under high wind power penetration rate. Simulation results show that the proposed methods achieve greater positive impact on the operational efficiency of ADNO than conventional methods. In addition, the proposed methods ensure the long-term profitability of ADNO, even with fluctuations in external factors.

Renewable energy effects on energy management based on demand response in microgrids environment

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications.