Dispatch Strategy Research Articles

Risk-averse energy dispatch for hybrid energy refueling stations considering Boundedly rational mixed user equilibrium and operational uncertainties

Nowadays, charging stations, hydrogen refueling stations, and natural gas refueling stations are being integrated into hybrid energy refueling stations (HERSs) to save investment costs and offer diverse refueling services altogether. This research proposes a risk-averse energy dispatch strategy for HERSs, incorporating bounded user rationality and operational uncertainties. A boundedly rational mixed user equilibrium model is proposed to capture the mobility of heterogeneous traffic flow, including electric vehicles (EVs), hydrogen fuel cell vehicles (HFCVs), and natural gas vehicles (NGVs), each with distinct refueling demands characterized by bounded decision-making rationality. A grid-connected HERS model integrated with carbon capture and storage and on-site renewable energy sources is designed to provide sustainable refueling services to EVs, HFCVs, and NGVs. Furthermore, an HERS operator aggregates multiple HERSs to participate in the day-ahead and real-time electricity markets. The optimal equipment operation and energy bidding strategies for the HERS operator are solved using a risk-averse approach based on conditional value-at-risk, addressing operational uncertainties from heterogeneous refueling demand, electricity markets, and renewable generation. Numerical results have validated the effectiveness of the proposed method in promoting net-zero emissions, securing economic benefits, and mitigating risks associated with operational uncertainties.

Sustainable truck platooning operations in maritime shipping: A data-driven approach

In liner shipping, stakeholders are increasingly committed to adopting autonomous and environmentally friendly transportation solutions, especially for truck operations managing container transfers. Beyond reducing labor costs, truck platooning technology—which enables autonomous trucks to operate in close formations, thereby significantly decreasing fuel consumption—promises to revolutionize fleets involved in maritime container transport. However, the potential of these benefits hinges on the process of developing and implementing optimization plans that address the specific challenges of container logistics, particularly in integrating truck platooning plans. In response to this need, this study extends the traditional instant-dispatch strategy by proposing a novel, data-driven dispatch strategy. We develop algorithms for both models and conduct extensive experiments focusing on truck operations for sea freight containers. Our findings reveal significant advantages of the data-driven dispatch strategy: it substantially reduces the total costs and fuel consumption associated with truck deliveries compared to the instant-dispatch strategy.

Distributed predefined-time optimal economic dispatch for microgrids

With the massive popularization of distributed generators, optimal economic dispatch has been a key optimization problem to maintain stable and efficient work of the whole system. In this paper, a new smooth reconstruction penalty function with continuous and piecewise linear differential is designed to deal with generation power constraints, which promotes to obtain a better suboptimal solution compared with the existing smooth penalty methods. A distributed predefined-time optimal economic dispatch strategy is presented by utilizing a time-based function. By employing the proposed strategy, the minimization of the generation cost with transmission loss under the power balance constraint and generation minimum/maximum constraints can be realized within a predefined settling time. The performance of the proposed optimization strategy is evaluated by simulations and hardware-in-the-loop experiments in terms of validity verification, robustness to load change and topology reconfiguration, plug-and-play functionality, and comparison with the existing results to illustrate the advantages of fast convergence and near optimal results.

Performance Analysis of a 50 MW Solar PV Installation at BUI Power Authority: A Comparative Study between Sunny and Overcast Days

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation’s dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system’s performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year’s worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month’s sunniest and cloudiest days. The goal is to aid in predicting the system’s output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant’s performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant’s performance from deteriorating. Optimizing a solar PV system’s design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability.

Hybrid-timescale optimal dispatch strategy for electricity and heat integrated energy system considering integrated demand response

An integrated energy system realizes multi-energy management and multi-load integrated supplement. However, because of the uncertainties of the renewable energy and loads and different dynamic characteristics of multi-energy, the efficiency of the system is constrained. To ascend the flexibility, a hybrid-timescale dispatch strategy considering integrated demand response is proposed. Firstly, a deep integrated demand response strategy is proposed using price demand response and ground source heat pump. Secondly, a linear dynamic model of the heat network is adopted to calculate the real-time energy transfer status in the heat pipeline, reflecting the thermal delay. Thirdly, to embed the linear dynamic model into the optimal scheduling process, a hybrid-timescale optimal dispatch framework for electricity and heat integrated energy system is proposed. Simulation results on MATLAB show that compare to the system that does not consider integrated demand response, the wind desertion, user cost and operation cost are reduced by 16.4 %, 0.85 %, and 2.72 %, respectively. And compared with the system only using energy storage, the planning cost is reduced by 47.0 %. By introducing the IDR strategy, and adopting the dynamic model of heat system, the flexibility of the system is released and the balance between economic cost and energy efficiency are achieved.

Analysis of power load tracking and regulation performance in a distributed multi-energy coupled system with nuclear and solar sources

This paper presents a novel distributed multi-energy coupled system that combines solar PV, nuclear power, and energy storage systems to address the power supply challenges in remote regions. Through parameter analysis and multi-objective optimization, the study aims to minimize the need for frequent reactor adjustments during system operation. The key findings indicate that by regulating the rotational speed of the main-compressor, it is possible to meet the power load requirements for a single nuclear power system. The battery capacity and the number of PV panels have a significant impact on the adjustment frequency of the reactor and the power output of the nuclear power system, respectively. The optimal battery capacity is determined to be 183 kWh, while the optimal number of PV panels is 327. These configurations result in an average power output of 20.86 kW for the PV system and 3458.28 kW for nuclear power system. To maximize the utilization of PV energy and minimize reactor adjustments, an energy dispatch strategy is proposed. With the optimal configuration, the adjustment frequency of the reactor decreases to 339 and 276 during the winter and summer months, respectively. Overall, this paper offers a feasible configuration and energy dispatch method for regional power supply.

A Cooperative Operation Strategy for Multi-Energy Systems Based on the Power Dispatch Meta-Universe Platform

To meet the challenges of renewable energy consumption and improve the efficiency of energy systems, we propose an intelligent distributed energy dispatch strategy for multi-energy systems based on Nash bargaining by utilizing the power dispatch meta-universe platform. First, the operational framework of the multi-energy system, including wind park (WP), photovoltaic power plant (PVPP), and energy storage (ES), is described. Using the power dispatch meta-universe platform, the models of WP, PVPP, and ES are constructed and analyzed. Then, a Nash bargaining model of the multi-energy system is built and transformed into a coalition profit maximization problem, which is solved using the alternating direction multiplier method (ADMM). Finally, the effectiveness of the proposed strategy is verified. The results show that the strategy greatly improves the consumption of renewable energy sources and the profit of the overall system.

An optimal dispatch strategy of off-grid park integrated energy system considering source volatility

An off-grid integrated energy system (IES) with hydrogen storage at park-level is proposed, utilizing wind, solar and natural gas as the main energy supply to replace fossil fuels, in order to overcome the insufficient consideration of energy source conversion and information exchange in the traditional energy system. Firstly, an IES schematic consisted with typical devices is proposed, and all the components are modelled in detail. Then, based on the renewable energy (RE) input requirements and the facility parameters, an optimal dispatch model for multi-energy conversion based on hydrogen storage has been demonstrated. In addition, an improved particle swarm optimization algorithm is also introduced to improve the strategy. Combined with the environmental characteristics, the multi-objective solution with the lowest economic cost and environmental cost, the highest energy supply reliability and energy efficiency as the optimization objectives is determined. Finally, a case study is conducted using the practical data of the Chongli Large-Scale Wind-Solar Complementary Coupled Hydrogen Production System Application Demonstration Project to validate the feasibility of the study under real conditions. The result shows that the RE consumption rate is greater than 99 % under the premise of considering the environmental cost and ensuring the operation reliability, which can effectively improve the operation economy and user economy of the power grid

A Cloud–Edge Collaborative Multi-Timescale Scheduling Strategy for Peak Regulation and Renewable Energy Integration in Distributed Multi-Energy Systems

Incorporating renewable energy sources into the grid poses challenges due to their volatility and uncertainty in optimizing dispatch strategies. In response, this article proposes a cloud–edge collaborative scheduling strategy for distributed multi-energy systems, operating across various time scales. The strategy integrates day-ahead dispatch, intra-day optimization, and real-time adjustments to minimize operational costs, reduce the wastage of renewable energy, and enhance overall system reliability. Furthermore, the cloud–edge collaborative framework helps mitigate scalability challenges. Crucially, the strategy considers the multi-timescale characteristics of two types of energy storage systems (ESSs) and three types of demand response (DR), aimed at optimizing resource allocation efficiently. Comparative simulation results evaluate the strategy, providing insights into the significant impacts of different ESS and DR types on system performance. By offering a comprehensive approach, this strategy aims to address operational complexities. It aims to contribute to the seamless integration of renewable energy into distributed systems, potentially enhancing sustainability and resilience in energy management.

Synergizing renewable energy sources in building-integrated hybrid energy systems via niche-ant colony optimization

The rapid advancement of Building-Integrated Hybrid Energy Systems (BIHES), characterized by renewable energy sources with variable generation patterns, presents significant challenges for effective integration and utilization. This paper introduces an optimal economic dispatch strategy for commercial BIHES using a Niche-Ant Colony Optimization (NACO) Algorithm. BIHES integrates renewable sources such as 200 kW photovoltaic panels, 150 kW wind turbines, and 500 kWh energy storage systems within commercial buildings. A detailed mathematical model for economic dispatch leverages a multi-load demand of 1.2 MW and flexible adjustment capabilities. The model aims to maximize on-site renewable energy use while balancing grid interaction. The NACO Algorithm, tested through comprehensive simulations, demonstrated significant improvements, resulting in a 12.96 % reduction in environmental costs, an 18.4 % reduction in the Levelized Cost of Energy (LCOE), and a 14.25 % reduction in total operational costs compared to traditional methods. Additionally, the system achieved annual CO2 savings of 25,000 tons, increased renewable energy absorption by 22 %, reduced grid dependency by 30 %, and decreased energy purchase costs by 20 %. Energy storage optimization also extended the storage system's lifecycle by 15 %. These results highlight the enhanced synergy and complementarity of multiple energy sources within BIHES, showcasing its potential for substantial economic and environmental benefits.

Low-carbon optimal dispatch strategy of power systems considering the combined operation of oxy-fuel combustion and electric hydrogen production

ABSTRACT Oxy-fuel combustion technology offers a promising solution for achieving nearly zero carbon dioxide emissions from coal-fired units, presenting a viable path toward low-carbon dispatch. This study establishes a simulation model for the low-carbon operation of power systems, focusing on the integrated operation of an oxy-fuel combustion plant and electric hydrogen production. By incorporating oxygen generated from electric hydrogen production into the oxy-fuel combustion plant, the primary objective is to minimize system operation costs. Through simulation analysis and verification, a comparative scenario is constructed to evaluate performance. The simulation results indicate that the joint operation approach yields significant benefits. In comparison to separate operation, the joint operation strategy leads to a reduction of approximately 7.38% in total system costs, 18.79% in operating costs, and 23.56% in carbon emissions. This integrated approach not only achieves lower carbon emissions but also results in reduced overall system costs.

Low-carbon economic dispatch strategy for integrated electrical and gas system with GCCP based on multi-agent deep reinforcement learning

Low-carbon economic dispatch strategy for integrated electrical and gas system with GCCP based on multi-agent deep reinforcement learning

Dispatch strategies for large-scale heat pump based district heating under high renewable share and risk-aversion: A multistage stochastic optimization approach

Heat pumps play an essential role in decarbonizing the heating sector, and their adoption is projected to rise significantly. The high share of large-scale heat pumps in district heating exposes heating utilities to uncertainty in electricity markets. This challenge is further exacerbated by 1) future high share of renewable energy resulting in increased uncertainty of electricity prices, and 2) introduction of voluntary energy bids for balancing energy markets (or regulation market). Despite the importance of this issue, little research has been conducted to address it. Therefore, this paper investigates the optimal dispatch of large-scale heat pump based district heating by adopting multi-stage stochastic optimization approach to minimize the total expected heat generation cost, taking sequential electricity markets prices as stochastic. We also evaluate the impact of high renewable energy penetration on this optimal dispatch strategy. Furthermore, we include the risk preference of district heating operator by using conditional value at risk in the objective function. We conclude that heat pump should be coupled with flexible technologies like electric boilers as more renewable energy and balancing market trading increases flexible units' dispatch. Also, most of the heat produced (50%) in our case study is used flexibly via thermal storage. We find more renewable could lead to negative expected costs and voluntary bids in balancing market further reduces the expected cost by 28–59%. Furthermore, trading risk increases significantly with renewable energy penetration, which can be mitigated to a limited extent by bidding in up and down balancing market. Risk aversion shifts trading from intraday to day-ahead market and promotes heat pump dispatch.

Parallel activation of helicopter and ground transportation after dispatcher identification of suspected anterior large vessel occlusion stroke in rural areas: a proof-of-concept case with modeling from the LESTOR trial

BackgroundWhen stroke patients with suspected anterior large vessel occlusion (aLVO) happen to live in rural areas, two main options exist for prehospital transport: (i) the drip-and-ship (DnS) strategy, which ensures rapid access to intravenous thrombolysis (IVT) at the nearest primary stroke center but requires time-consuming interhospital transfer for endovascular thrombectomy (EVT) because the latter is only available at comprehensive stroke centers (CSC); and (ii) the mothership (MS) strategy, which entails direct transport to a CSC and allows for faster access to EVT but carries the risk of IVT being delayed or even the time window being missed completely. The use of a helicopter might shorten the transport time to the CSC in rural areas. However, if the aLVO stroke is only recognized by the emergency service on site, the helicopter must be requested in addition, which extends the prehospital time and partially negates the time advantage. We hypothesized that parallel activation of ground and helicopter transportation in case of aLVO suspicion by the dispatcher (aLVO-guided dispatch strategy) could shorten the prehospital time in rural areas and enable faster treatment with IVT and EVT.MethodsAs a proof-of-concept, we report a case from the LESTOR trial where the dispatcher suspected an aLVO stroke during the emergency call and dispatched EMS and HEMS in parallel. Based on this case, we compare the provided aLVO-guided dispatch strategy to the DnS and MS strategies regarding the times to IVT and EVT using a highly realistic modeling approach.ResultsWith the aLVO-guided dispatch strategy, the patient received IVT and EVT faster than with the DnS or MS strategies. IVT was administered 6 min faster than in the DnS strategy and 22 min faster than in the MS strategy, and EVT was started 47 min earlier than in the DnS strategy and 22 min earlier than in the MS strategy.ConclusionIn rural areas, parallel activation of ground and helicopter emergency services following dispatcher identification of stroke patients with suspected aLVO could provide rapid access to both IVT and EVT, thereby overcoming the limitations of the DnS and MS strategies.

Multi-objective sizing and dispatch for building thermal and battery storage towards economic and environmental synergy

The role of building thermal and battery storage is pivotal in advancing smart cities and achieving sustainability goals through effective energy management. Despite their significance, there are several limitations in the sizing approach and value stream analysis with various objectives for their widespread adoption in buildings. This work proposes a flexible and scalable multi-objective optimization framework for optimal sizing and dispatch of building thermal and battery storage, addressing multiple objectives simultaneously using mixed-integer linear programming. The weighted-sum method is adapted, combining multiple objectives into a single function. The two-stage procedure iterates over different weights, generates optimal solutions, and forms the Pareto front. Case studies are performed to assess the energy, economic, and environmental benefits of building energy storage systems for a large office building in three climate locations. The results demonstrate that the proposed framework efficiently determines optimal sizing and dispatch strategies, addressing the balance between economic viability and emission reduction. The dynamic relationship between time-of-use energy charges and emission factors leads to diverse strategies based on whether economic or environmental concerns are prioritized. This research enhances our understanding of the benefits of thermal and battery storage systems in buildings, providing valuable guidance to stakeholders.

Low-carbon economic dispatch strategy for microgrids considering stepwise carbon trading and generalized energy storage

Integrating carbon trading mechanisms with generalized energy storage (GES) fully embodies the principles of green and coordinated development, serving as a crucial means to achieve low-carbon construction of microgrids. This research presents a strategy for optimizing energy allocation within microgrids to minimize carbon emissions and enhance microgrid systems' economic-environmental benefits. The strategy takes into account the use of tiered carbon trading and GES. Based on a typical microgrid system architecture, an economic dispatch model for microgrids is developed, which integrates renewable energy sources such as wind and solar storage, gas turbines, energy storage systems, and flexible resources on the demand side. The model aims to minimize carbon emissions while optimizing the allocation of resources. Subsequently, the model facilitates microgrid carbon emission control by considering the transferable, convertible, and reducible properties of GES. Furthermore, implementing a tiered carbon trading mechanism decreases carbon emissions. Finally, using a real microgrid example from a specific region in China, the results indicate that the proposed method significantly enhances the system's low-carbon level. Notably, compared to scenarios that do not consider GES, the proposed method substantially reduces total costs by 6.62% and decreases carbon emissions by 22.2%. The findings indicate that the suggested dispatch model can substantially decrease carbon emissions while simultaneously improving the economic efficiency of the microgrid system.

Real-Time Simulation System for Small Scale Regional Integrated Energy Systems

Regional Integrated Energy Systems (RIESs) integrate wide spectrum of energy sources and storage with optimized energy management and further pollution reduction. This paper presents a real-time simulation system for RIESs powered by multiple digital signal processors (DSPs) with different means of data exchange. The RIES encompasses the DC microgrid (DMG), the district heat network (DHN), and the natural gas network (NGN). To realize multi-energy flow simulation, averaged switch models are investigated for different types of device-level units in the DMG, and the unified energy path method is used to build circuit-dual models of the DHN and NGN. A hierarchical island strategy (HIS) and a multi-energy dispatch strategy (MEDS) are proposed to enhance the energy flow control and operating efficiency. The two-layer HIS can adjust the operating status of device-level units in real time to achieve bus voltage stability in the DMG; MEDS uses energy conversion devices to decouple multi-energy flows and adopts the decomposed flow method to calculate the flow results for each network. The real-time simulation hardware platform is built, and both electricity-led and thermal-led experiments are carried out to verify the accuracy of models and the effectiveness of the proposed strategy. The proposed system with an energy management strategy aims to provide substantial theoretical and practical contributions to the control and simulation of RIESs, thus supporting the advancement of integrated energy systems.

A multi-timescale and chance-constrained energy dispatching strategy of integrated heat-power community with shared hybrid energy storage

The community in the future may develop into an integrated heat-power system, which includes a high proportion of renewable energy, power generator units, heat generator units, and shared hybrid energy storage. In the integrated heat-power system with coupling heat-power generators and demands, the key challenges lie in the interaction between heat and power, the inherent uncertainty of renewable energy and consumers’ demands, and the multi-timescale scheduling of heat and power. In this paper, we propose a game theoretic model of the integrated heat-power system. For the welfare-maximizing community operator, its energy dispatch strategy is under chance constraints, where the day-ahead scheduling determines the scheduled energy dispatching strategies, and the real-time dispatch considers the adjustment of generators. For utility-maximizing consumers, their demands are sensitive to the preference parameters. Taking into account the uncertainty in both renewable energy and consumer demand, we prove the existence and uniqueness of the Stackelberg game equilibrium and develop a fixed-point algorithm to find the market equilibrium between the community operator and community consumers. Numerical simulations on integrated heat-power community show that the proposed solution outperforms the constant curtailment strategy with the daily cost reduced by 14.38%.

Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market

This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, focusing on enhancing profitability while factoring in battery degradation. This study incorporates the effects of battery degradation on the dynamics in the optimisation framework. Considering this cost in economic analysis and operational strategies is essential to optimise long-term performance and economic viability. Neglecting degradation costs can lead to suboptimal operation and dispatch strategies. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximising net revenues, maximising profits considering capacity degradation, and maximising profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. This paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.

Optimal dispatch strategy of battery energy storage system in utility-scale photovoltaic integrated grid under variability

The frequency response of a photovoltaic (PV) system integrated power grid is severely hampered due to inadequate inertial support. Integrating a battery energy storage system (BESS) can assist in maintaining frequency response by providing a rapid injection of active power into the grid. Nevertheless, instead of typical constant droop settings enabled in BESS, adopting variable PV power dependent operation of BESS can ensure frequency stability with reduced injection, especially in PV-integrated grids. From this viewpoint, this paper proposes a novel frequency control approach of BESS depending on the available PV power in the grid. A gradient descent-based optimization is utilized to evaluate the required maximum injection from the installed BESS to maintain frequency response under different PV generation values. This approach is then tested by modifying the BESS frequency controller to keep the active power dispatch from BESS within the calculated value under a given outage. The whole methodology is analyzed in a customized IEEE 39 bus New England System under different PV penetration and generation scenarios. The results indicate successful convergence to optimal dispatch levels for the BESS across all evaluated scenarios. As PV generation increases, the optimal BESS dispatch as a percentage of rated PV output declines gradually. However, at lower PV generation, this value rises rapidly and reaches the installed BESS capacity. Later, the applicability of the methodology is validated by comparing the simulated outputs with different test grid network and another support technique- Synchronous Condenser (SC). From the standpoint of frequency stability, the proposed technique can assist the grid planners in regulating BESS operations in a renewable integrated grid more effectively.