Optimal Operation Strategy Research Articles

On the economic feasibility of tidal range power plants

The potential energy associated with tides presents a sustainable energy resource that remains largely untapped. Uncertainties on the economic case of tidal range power plants are a known obstacle. Research on tidal range structures suggests energy yield may be maximized through operation strategy optimization, and that impacts can be mitigated through design optimization. While instructive, these perspectives alone are insufficient to support the feasibility of individual projects. We integrate operation optimization and hydrodynamic impact analyses within a cost evaluation framework for tidal range structures focusing on capital costs (CAPEX) and levelized cost of energy (LCOE). Once benchmarked against 11 historic proposal cost projections, we perform a redesign of 18 tidal power plants to deliver a comprehensive comparative basis across a diverse range of sites in the UK. Tidal power plant operation is simulated in regional shallow-water equation models, acknowledging tide variability. The cost evaluation framework demonstrates the impact of geospatial variations on key cost components. The redesign process indicates transformative implications in that equivalent and lower LCOE values can be achieved for designs at a substantially lower CAPEX. Given how the latter hinder development, we show how tidal range schemes could be far more economically feasible than commonly perceived.

Optimal operation strategies for freight transport with electric vehicles considering wireless charging lanes

Unlike traditional recharging at fixed stations, wireless charging lanes equipped on the road network enable electric vehicles (EVs) to recharge while in motion, reducing the time required for EV recharging. Indeed, the availability of wireless charging lanes allows the urban freight transport service operators to consider adjusting EVs routing plan to do EV recharging in transit so that the total operational costs can be further reduced. Envisioning that wireless charging lanes would be practically constructed on the road network, this study aims to investigate how the optimal EV routing plan can be determined in a pickup and delivery problem in the future electrified mobility system. The problem is formulated into a mixed-integer programming model, and we propose a branch-and-price algorithm to solve it. Additionally, a large neighborhood search heuristic is incorporated within the column generation framework to effectively tackle the sub-problems. Computational experiments on test instances highlight the effectiveness of our proposed algorithm and demonstrate the potential of wireless charging lanes to reduce total operational costs.

Integrated Modeling and Optimal Operation Strategy of Building Cooling System Combining the Standardized Thermal Resistance and Genetic Algorithm

ABSTRACTIntegrated modeling and operation optimization of building energy systems is significant for improving the energy utilization efficiency and reducing carbon emission. This paper introduces the standardized thermal resistance to construct an overall heat current model of the building cooling system with coupled heat transfer, mass transfer, and energy conversion processes. Based on the heat current model, we derive the holistic thermal energy transfer and conversion constraints on the system level and reduce the intermediate parameters of the system model. Moreover, the genetic algorithm is introduced to optimize the system operation conditions under the given system structure parameters. The optimization results provide the optimal mass flow distribution of cooling water, return water, and ambient air and meanwhile show that the compressor power consumption can reach 76.5% of the total system power consumption. The change of user behavior by raising the room temperature to 4°C can reduce the total system power consumption by 20%. The results are in line with the theoretical reality and prove the feasibility and effectiveness of the method proposed in this paper, which provides a practical reference for the energy‐saving operation of the building cooling system.

Optimization Operation Strategy for Comprehensive Energy System Considering Multi-Mode Hydrogen Transportation

The transformation from a fossil fuel economy to a low-carbon economy has reshaped the way energy is transmitted. As most renewable energy is obtained in the form of electricity, using green electricity to produce hydrogen is considered a promising energy carrier. However, most studies have not considered the transportation mode of hydrogen. In order to encourage the utilization of renewable energy and hydrogen, this paper proposes a comprehensive energy system optimization operation strategy considering multi-mode hydrogen transport. Firstly, to address the shortcomings in the optimization operation of existing systems regarding hydrogen transport, modeling is conducted for multi-mode hydrogen transportation through hydrogen tube trailers and pipelines. This model reflects the impact of multi-mode hydrogen delivery channels on hydrogen utilization, which helps promote the consumption of new energy in electrolysis cells to meet application demands. Based on this, the constraints of electrolyzers, combined heat and power units, hydrogen fuel cells, and energy storage systems in integrated energy systems (IESs) are further considered. With the objective of minimizing the daily operational cost of the comprehensive energy system, an optimization model for the operation considering multi-mode hydrogen transport is constructed. Lastly, based on simulation examples, the impact of multi-mode hydrogen transportation on the operational cost of the system is analyzed in detail. The results indicate that the proposed optimization strategy can reduce the operational cost of the comprehensive energy system. Hydrogen tube trailers and pipelines will have a significant impact on operational costs. Properly allocating the quantity of hydrogen tube trailers and pipelines is beneficial for reducing the operational costs of the system. Reasonable arrangement of hydrogen transportation channels is conducive to further promoting the green and economic operation of the system.

Stackelberg game-based optimal scheduling of two-level virtual power plant

In order to rationally solve the problem of distributing the interest of each decision-making body in the transaction of virtual power plant (VPP) participating in the energy market, this paper introduces game theory into the VPP energy system (VPPES) and makes a more in-depth study on its joint scheduling on the basis of economy and low carbon. First, this paper constructs a bi-level VPP system with a new type of VPP and studies its optimal operation strategy under the Stackelberg game framework. The strategy takes the energy seller as the leader and the VPP supplier and the loader as the followers, and the upper and lower layers optimize the seller’s pricing strategy, the VPP’s output plan, and the user’s demand at the same time through real-time information exchange and loop iteration. Second, the energy trading process and mathematical model of the bi-level VPP system are introduced, and it is proved that there exists a unique Stackelberg equilibrium in the proposed game model, which is optimally solved by using an improved coyote optimization algorithm combined with a solver. Finally, it is verified through examples that the proposed operation strategy can significantly reduce the generation cost of the VPP and maximize the benefit of the seller and the utility of the loader, so as to realize economic energy supply and scientific energy use, which can provide a new paradigm for the economic and environmental development of the new energy system.

Optimal Control Strategies for Reliable Operation of Electric Vehicle Charging Stations under Fault Tolerant Scenarios

Optimal Control Strategies for Reliable Operation of Electric Vehicle Charging Stations under Fault Tolerant Scenarios

Multi-agent modeling for energy storage charging station scheduling strategies in the electricity market: A cooperative learning approach

With integration of an energy storage system (ESS), an energy storage charging station serves as pivotal intermediaries between the smart grid and electric vehicles (EVs). This station utilizes the ESS to enhance grid stability and facilitate energy management. Participation in electricity market transactions offers revenue opportunities for charging stations, but it also introduces operational challenges, due to fluctuating electricity market prices and diverse energy demands and supplies. In this paper, we study the operation strategy optimization problem for the charging station, addressing economic and service challenges influenced by market volatility and energy diversity. The optimization objective considers not only maximizing economic benefits from the electricity market and EV services but also minimizing penalties associated with EV service quality. We propose a model that accounts for the dynamics of the electricity market, uncertainties from EV demands, and disturbances from green power generation, optimizing the power scheduling of the ESS and multiple charging piles (CPs) to determine transaction power in the market. The cooperative scheduling strategies for the ESS and CPs are learned using the proposed heterogeneous Multi-agent Deep Deterministic Policy Gradient method. This approach features distributed agents learning to determine decision variables for both the ESS and CPs, while a joint critic network assesses the station’s overall objectives to guide their cooperative learning. The proposed method was tested against three state-of-the-art benchmark methods, which showed our method achieves better results.

AC energy islands for the optimal integration of offshore wind energy resources: Operation strategies using multi-objective nonlinear programming

The increasing need for integrating offshore wind generation into power systems has highlighted energy islands as a promising solution. Such islands also could incorporate responsive infrastructure for improving grid flexibility such as energy storage and hydrogen production. AC energy islands could be particularly cost–benefit effective for short and medium distances between wind power parks and onshore grids. However, the implementation of AC energy islands presents challenges from the viewpoints of voltage stability, power dispatch and reliable operation. This paper develops optimal operation strategies for AC energy islands including: i) a nonlinear optimal power flow approach for controlling the reactive power compensation systems; ii) a finite horizon mathematical programming approach for the management of BESS and hydrogen production systems; and iii) a multi-objective programming approach for maximizing the active power delivered to the onshore power grid while minimizing the nodal voltage deviations simultaneously. Results show that the single-objective approach for real and reactive power dispatch reduces voltage deviations by around 20 % and line currents by over 70 % in the evaluated test system, compared to the non-optimal scenario. Moreover, the incorporation of energy storage in AC energy island test system increased the capacity for power dispatch over 10 %. Additionally, the multi-objective approach is effective in aligning the nodal voltages with nominal values and maximizing the power delivered to the onshore grid. Results show that with a small 1% reduction in power delivery, the proposed multi-objective approach achieved an approximate 50 % decrease in nodal voltage compared to the single-objective approach.

An MIQCP-based multi-objective optimal operation strategy for renewables-integrated smart distribution systems considering transformer loss of life and environmental emissions

Owing to growing electricity demand and environmental concerns, renewable-energy-based distributed generations (DGs) are widely integrated into today's distribution systems. However, high penetration of renewables DGs (RDGs) requires additional investments in high-cost step-up transformers connecting RDGs to the grid. On the other side, regarding variability of RDGs’ output power, the connecting transformer's capacity is not completely utilized in a considerable time period. In this paper, a smart-grid technology-based scheduling model is proposed to efficiently utilize the free capacity of existing transformers without investing on new ones. The model is based on dynamic thermal rating (DTR) and loss of life (LOL) feature of transformer. The developed approach is formulated as a mixed-integer quadratic constrained programming (MIQCP) as a convex model to obtain global optimum solutions. The conducted model is treated as a multi-objective optimization with LOL minimization as well as emission reduction. The proposed multi-objective model is implemented using ɛ-constraint method where the final solution is selected by fuzzy-satisfying and max-min techniques. The model is implemented in GAMS and the optimization is accomplished by GUROBI solver to show efficacy of the proposed approach. The simulations demonstrate the capability of conducted approach in managing the CO2 emission as well as transformer loss of life. Among different Pareto solutions, the best solution reduces the CO2 emission by 11.7% while the transformer's aging is decreased by 19.2% demonstrating environmental, economic, and technical advantages of the proposed model.

Dynamic Modeling of Polymer Electrolyte Membrane Water Electrolysis System and Dynamic Response

Water electrolysis to produce hydrogen using electric energy is classified into polymer electrolyte water electrolysis, alkaline water electrolysis, solid oxide water electrolysis, and anion exchange membrane water electrolysis according to the type of electrolyte applied. Among them, polymer electrolyte membrane water electrolysis has a lower operating temperature than other types of water electrolysis, and it is possible to operate high-efficiency, high-power density hydrogen production with high current density. In addition, polymer electrolyte water electrolysis has the advantage of fast response to changes in power supply, so empirical studies for green hydrogen production linked to renewable energy are being conducted most actively compared to other water electrolysis. However, research on coupling polymer electrolyte water electrolysis with renewable energy generation with intermittent and irregular power generation has mainly focused on materials, cells, and stacks. In order to keep the hydrogen production reaction of water electrolysis stack stable under the condition of supply power fluctuation, the peripheral devices should be optimally installed and controlled.In this study, a dynamic model of a polymer electrolyte water electrolysis system was developed using Aspen HYSYS®. To accurately predict the hydrogen production efficiency, a stack model considering electrochemical reactions and crossovers through the electrolyte was developed and internalized in a spread sheet. A heat exchanger model from the model library was used to simulate dynamic changes in the electrolyte temperature flowing through the water electrolysis stack. The developed stack model was extended to a system model by integrating the feed pump, separator, condenser, heater, and cooling system. The developed water electrolysis system model was simulated in connection with power ramp variations and renewable energy output, and the optimal operation strategy of each module unit was derived to ensure the stability of high-purity hydrogen production under transient conditions.

Principles of operational optimization of CSP plants based on carbon dioxide mixtures

This research, developed in the framework of the SCARABEUS project, studies the off-design performance of transcritical power cycles running on C2-S2 mixtures in Concentrated Solar Power applications. The objective of this work is to identify optimum operational strategies that maximize net energy production when exposed to variable ambient temperature, with special focus on the operation of the Heat Rejection Unit (Air-Cooled Condenser).Four different strategies are identified, depending on ambient temperature: variable or constant condensation pressure for ambient temperatures lower than the design value, and constant turbine inlet temperature or constant return temperature of the heat transfer fluid for ambient temperature higher than design value. The results show that a combination of variable and constant minimum cycle pressure stands as the most promising alternative for low ambient temperatures, enabling net system efficiencies higher than 41%. As a drawback, this strategy poses potential issues on the control of the compressor inlet, which must be further investigated in future works. On the other hand, constant turbine inlet temperature enables higher net performance than constant return temperature of the heat transfer fluid, even if at the expense of a reduction in energy storage capacity for the same inventory of molten salts and the creation of detrimental thermal transients. Finally, it is found that the Air-cooled condenser configuration with all fans equipped with Variable Frequency Driver stands as the best alternative to maximize the techno-economic performance of the system.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Enhancing periodic mobility planning: ARIMA-driven prognostications for subway passenger volume dynamics

Abstract. This study aims to accurately predict urban subway passenger flow using the Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto Regressive Integrated Moving Average (SARIMA) model, with the intention of providing a scientific basis for subway operation management. By meticulously sorting and analyzing the existing traffic data of a large city's subway system, this research constructs a model that comprehensively considering the seasonal, trend, and stochastic characteristics of time series data. During the model construction process, data preprocessing is carried out, including stationarity tests, differencing, model order determination, and parameter estimation, ultimately identifying an ARIMA and SARIMA model with good fitting results, effectively identifying and forecasting characteristics such as numerical fluctuations. Additionally, this study conducts a special analysis of the prediction effects at diverse time scales, verifying the applicability and superiority of the ARIMA and SARIMA model in subway passenger flow prediction. This research not only provides a powerful tool for passenger flow prediction for subway operation management departments, aiding in the reasonable allocation of capacity and optimization of operation strategies, but also holds significant reference and application value for the passenger flow prediction and management of other urban public transportation systems.

Optimal operating strategy of hybrid heat pump − boiler systems with photovoltaics and battery storage

The growing need to reduce energy consumption and greenhouse gas emissions is driving the search for more efficient heating solutions in buildings. Hybrid heating systems, which combine air-to-water heat pumps (AWHP) with traditional gas boilers, are a common solution after refurbishment investments. However, managing these systems effectively, particularly when integrated with photovoltaic (PV) panels and battery energy storage systems (BESS), remains a complex task. For instance, heat pumps perform poorly in very cold conditions, making boilers a more efficient option; however, it might be advantageous to use it to increase electricity self-consumption. Optimal management depends on multiple factors, including future forecast data. In this paper, a daily optimization program is developed by means of a brute-force approach using forecast data. The core innovation of this paper is the use of an artificial neural network (ANN) that, trained on predictive optimization results, can determine the optimal solution in real-time without the need for future forecasts. The ANN achieved a 99.16% accuracy in new scenarios, successfully optimizing costs, CO2 emissions, and primary energy use. Results indicate up to 19% cost savings in colder cities, a 12% reduction in CO2 emissions, and a 3% decrease in primary energy consumption. This approach holds significant potential for enhancing the integration of renewable energy sources, contributing to long-term sustainability goals.

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

Operation strategy and capacity optimization of wind-solar hydrogen storage system considering the characteristics of electric hydrogen production

Abstract Wind and solar power for hydrogen production can convert fluctuating renewable energy into high-quality hydrogen, but their intermittency leads to frequent starts and stops of electrolyzers and low integration rates of renewable energy. This study proposes operational strategies and capacity optimization methods for a hybrid hydrogen production system BY using alkaline electrolyzers (AEL) and proton exchange membrane electrolyzers (PEMEL). Compared to conventional approaches, the optimization achieves an increase in hydrogen production and a reduction in power fluctuations, offering insights for enhancing the coordinated operation of wind-solar hydrogen production systems.

Energy consumption characteristics and evaluation of public buildings in Tianjin, China

In order to analyze the impact of heat source type and building function on the energy consumption level and distribution characteristics, find out the existing problems, and provide scientific guidance for the selection of heat source type and the optimization of operation strategy of public buildings in Tianjin, China, the building structure information, cold and heat source type and operation energy consumption data of 195 public buildings are collected by on-site investigation and statistics. The heating energy consumption and non-heating energy consumption are analyzed by graphical method and quartile method. The results show that the heat source of urban central heating accounted for the highest proportion, 63.07%. The building energy consumption using coal-fired boiler is the highest, which is 109.61 kWh/(m2.a), and the building energy consumption using ground source heat pump is the lowest, which is 31.90 kWh/(m2.a). In different types of public buildings, the energy consumption of school buildings is the lowest, which is 24.53 kWh/(m2.a), and the distribution is the most concentrated, while the energy consumption of cultural places is the highest, which is 81.64 kWh/(m2.a), and the distribution is the most scattered. Through on-site investigation, some problems in building envelope, energy supply equipment and operation methods are found out, and corresponding energy-saving measures are put forward, which provides guidance for reducing energy consumption of public buildings.

Distributed transaction method for multi-energy systems clusters considering uncertainties in photovoltaic power output

Abstract In light of the emerging trend of distributed transactions within the multi-energy system(MES) cluster, this paper proposes a distributed transaction method for MES that takes into account the uncertainties in photovoltaic (PV) power output. This approach aims to achieve optimized resource scheduling within the MESs, as well as coordinated optimization of transactions between different MESs. Firstly, the uncertainties in PV power output within the MES are addressed using the distributionally robust chance constraint. Subsequently, the optimization problem of the MES cluster is decomposed using the alternating direction multiplier method, thereby establishing a distributed transaction model for the MES cluster that considers PV power output uncertainties. Finally, the case study demonstrates that the proposed model can balance the individual interests of each MES with the overall interests of the MES cluster. It effectively reduces the operating costs of the MESs and determines optimal operation strategies for them.

Research on energy interactive operation optimization strategy of multi-energy network with integrated energy based on cooperative game

Abstract Integrated energy includes multi-energy networks such as electricity, gas, cold, and heat. Horizontal multi-energy coordination and vertical source network storage load interaction can effectively support large-scale new energy consumption. In this paper, a multi-energy flow model of the integrated energy multi-energy network is constructed, an optimization model of energy sharing and mutual benefit of the multi-energy network is established based on Nash negotiation, and then the model is solved to obtain the optimal distribution scheme of cooperation benefits. Finally, a numerical example is given to verify the proposed energy sharing and mutual benefit method. The results show that the total power generation cost of each park is reduced by 27%, and the profit distribution of each park is more reasonable.

Experimental evaluations of Berkeley thermal sensation and comfort models in electric vehicle cabin under cold outdoor conditions

As the automobile industry is transitioning toward electric vehicles, manufacturers have started implementing local warmers alongside cabin heating, ventilation, and air conditioning (HVAC) systems for effective thermal comfort management. However, optimal operating strategies need to be developed for integrating local warmers with HVAC systems. Although the Berkeley models comprising local/overall thermal sensation and comfort models offer insights in this regard, they lack follow-up assessments for occupants transitioning from very cold states. In this study, Berkeley models were evaluated using two sets of experimental data collected in a transient vehicle cabin under cold outdoor conditions: test (A) with cabin HVAC alone and test (B) with both HVAC and local warmers. The findings confirm the satisfactory performances of the Berkeley models for predicting overall sensation and comfort, with a maximum root mean-squared error (RMSE) of 0.15. The local comfort model performed poorly with the original coefficients across both datasets (maximum RMSE of 1.96). Therefore, the model coefficients were regressed for the dataset from test A and validated against the dataset from test B to achieve a maximum RMSE of 0.49. With these regressed coefficients, it was observed that moving toward a neutral whole-body state diminished the potential to maximize local comfort. Conversely, the local sensation model showed poor agreement (maximum RMSE of 1.9); we confirmed that accurate adaptive setpoint temperatures are a prerequisite for ensuring good predictions from the model. These findings are expected to contribute toward future efforts in using Berkeley models to formulate effective local warmer–HVAC operational strategies in electric vehicles.