Energy Dispatch Research Articles

The Value of Energy Storage in Facilitating Renewables: A Northeast Area Analysis

The cross-regional and large-scale transmission of new energy power is an inevitable requirement to address the counter-distributed characteristics of wind and solar resources and load centers, as well as to achieve carbon neutrality. However, the inherent stochastic, intermittent, and fluctuating nature of wind and solar power poses challenges for the stable bundled dispatch of new energy. Leveraging the regulation flexibility of energy storage offers a potential solution to mitigate new energy fluctuations, enhance the flexibility of the hybrid energy systems, and promote bundled dispatch of new energy for external transmission. This paper takes energy storage as an example and proposes a capacity configuration optimization method for a hybrid energy system. The system is composed of wind power, solar power, and energy storage, denoted by the wind–solar–energy storage hybrid energy systems. The objective is to quantify the support provided by energy storage to bundled dispatch of new energy, namely determining the new energy transmission capacity that can be sustained per unit of energy storage. The results demonstrate that the proposed method effectively improves the bundled dispatch capacity of new energy. Moreover, the obtained configuration results can be tailored based on different wind–solar ratios, allowable fluctuation rates, and transmission channel capacities, rendering the approach highly valuable for engineering practicality.

Multi-interval rolling-window joint dispatch and pricing of energy and reserve under uncertainty

In this paper, the intra-day multi-interval rolling-window joint dispatch and pricing of energy and reserve is studied under increasing volatile and uncertain renewable generations. A look-ahead energy-reserve co-optimization model is proposed for the rolling-window dispatch, where possible contingencies and load/renewable forecast errors over the look-ahead window are modeled as several scenario trajectories, while generation, especially its ramp, is jointly scheduled with reserve to minimize the expected system cost considering these scenarios. Based on the proposed model, marginal prices of energy and reserve are derived, which incorporate shadow prices of generators’ individual ramping capability limits to eliminate their possible ramping-induced opportunity costs or arbitrages. We prove that under mild conditions, the proposed market design provides dispatch-following incentives to generators without the need for out-of-the-market uplifts, and truthful-bidding incentives of price-taking generators can be guaranteed as well. Some discussions are also made on how to fit the proposed framework into current market practice. These findings are validated in numerical simulations.

Integrated Energy Management of Smart Grids in Smart Cities Based on PSO Scheduling Models

The supporting position and role of smart grids in the construction of smart cities have not been fully explored. Based on systematizing the system architecture of smart cities, we first analyze the facilitating and constraining roles of smart grids and smart cities with each other and make a quantitative analysis of the coordination and supporting roles between them; in the smart grid environment, we propose a framework of energy management system based on particle swarm optimization (PSO) dispatching model. The algorithm optimizes the operation of dispatchable loads, electric vehicles, and energy storage systems based on outdoor temperature forecasts, renewable energy power output forecasts, day-ahead tariff signals, and customer preferences to minimize customer electricity costs. The performance of the algorithm is verified through simulation experiments, and the results show that the proposed algorithm significantly reduces electricity consumption costs by 32.54% compared to algorithms that only optimize the scheduling of loads or some components of the home energy management system.

Hierarchical Control of Megawatt-Scale Charging Stations for Electric Trucks With Distributed Energy Resources

Electrifying medium- and heavy-duty trucks is critical to decarbonizing the transportation sector. Energy needs of electric trucks will likely require megawatt-scale charging stations, which could significantly stress the electric distribution grid. Distributed energy resources (DER) can alleviate this stress and reduce charging costs with proper management. To that end, this work develops a hierarchical predictive control algorithm for future multi-port megawatt-scale charging stations that can provide real-time energy management for stations, decide charging rates, dispatch energy storage system (ESS), and provide grid voltage support. We integrate three algorithmic components: (i) an energy management optimization (EMO) that provides supervisory control to DER assets and charging loads at minute scale, (ii) a real-time energy management system (RT-EMS) that heuristically compensates for fast disturbances at sub-second scale, and (iii) a model predictive control (MPC)-based battery management system (BMS) that communicates future charging demands to the EMO, to manage the overall megawatt-scale site. Validation in a controller hardware-in-the-loop (CHIL) environment shows that the hierarchical controller can reduce the total energy consumption from the grid by approximately 28% compared to an uncontrolled case for the station configuration in this paper, without impacting charging time.

Designing and performance assessment of a hybrid CAES based CHP system integrated with solar energy and heat pump

In this paper, a novel combined heat and power system based on compressed air energy storage (CAES) coupled with a solar collector system was introduced. Based on the significant effect of heat pumps in improving heat quality, the heat pump and solar energy are adopted to improve energy storage and economic performance. To maximize the effective utilization of solar energy, the energy dispatch scheme of the system has been studied in detail. The system's performance under multiple operating conditions has been evaluated. Moreover, to further explore the optimization potential of the solar-coupled compressed air energy storage system, important factors such as the distribution proportion of thermal oil and key temperature have been emphasized and analyzed. The optimization of key temperature, distribution proportion of thermal oil, and operating conditions can enhance the cascade utilization of energy, and also provide a comprehensive approach for the design and optimization of the proposed system. Parametric sensitivity analysis was first conducted. Results show that the charging pressure should be close to the discharging pressure and there is no local optimal distribution ratio for the thermal oil. The optimal condition under design constraints is also determined, the system power efficiency, energy efficiency, and power-to-power efficiency reached 0.297, 0.851, and 0.570, while the levelized cost of storage of the system is 0.527 $/kWh, the dynamic payback period is 9.516 and simple payback period is 7.428 under the specified conditions. The output power and heat in 6 h of discharging are 3829.50 kWh and 7147.48 kWh, respectively, with a total investment cost of 7373.57k$. Moreover, the exergy and economic analyses were then conducted. The highest exergy losses were caused by the solar collector system with 29.01% of the total loss. The turbines and the solar system also had the highest economic cost, indicating directions for further optimization.

Techno-economic comparison of different energy storage configurations for renewable energy combined cooling heating and power system

Amidst the growing imperative to address carbon emissions, renewable energy combined cooling heating and power (RCCHP) systems have emerged as a transformative alternative to their fossil fuel-driven counterparts. Given the intermittent and volatile nature of renewable energy, the integration of energy storage technology has taken center stage in the exploration of RCCHP systems. This paper presents a quantitative techno-economic assessment of seven prominent energy storage configurations, including battery (BAT), thermal energy storage (TES), hydrogen storage (HS), and their combinations within the context of RCCHP systems. To avoid potential deviations caused by the rule-based energy dispatch strategy, the optimization problem is solved by a deterministic tool named mixed integer linear programming (MILP). Meanwhile, an accuracy assessment is performed on various time series aggregation methods to ensure the reliability of optimization outcomes. The key results revealed that the optimal storage configuration varies with different self-sufficiency rate (SSR) requirements. The BAT+HS+TES emerges as the most cost-effective configuration to implement 100% SSR requirements. The annualized total cost (ATC) of BAT+HS+TES configuration is 11% ∼ 39.5% lower than other single or combined energy storage configurations. In addition, HS stands as a more economical technology for countering the off-grid effect than BAT or TES. The underestimated ATC for the RCCHP system enlarges with escalating SSR requirements when using inappropriate aggregation methods. The maximum underestimation of ATC reaches −34.8% under the 100% SSR requirement when only using one typical day from each season.

Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty

Given the steep rises in renewable energy's proportion in the global energy mix expected over several decades, a systematic way to plan the long-term deployment is needed. The main challenges are how to handle the significant uncertainties in technologies and market dynamics over a large time horizon. The problem is further complicated by the fast-timescale volatility of renewable energy sources, potentially causing grid instability and unfulfilled demands. As a remedy, energy storage and power-to-hydrogen systems are considered in conjunction with energy management system but doing so raises the complexity of the planning problem further. In this work, the long-term capacity planning for a hybrid microgrid (HM) system is formulated as a multi-period stochastic decision problem that considers uncertainties occurring at multiple timescales. Long-term capacity decisions are inherently linked with energy dispatch and storage decisions occurring at fast-timescale and it is best to solve for them simultaneously. However, the computational demand for solving it becomes quickly intractable with problem size. To this end, we propose to develop a Markov decision process (MDP) formulation of the problem and use simulation-based reinforcement learning for multi-period capacity investments of the planning horizon. The MDP includes the policies used for dispatch and storage operation, which are represented by linear programming as a part of the simulation model. The effectiveness of our proposed method is demonstrated with a case study, where decisions over multiple decades are considered along with various uncertainties of multi-timescales. Economic and environmental assessments are performed, providing valuable guidelines for government's energy policy.

Estimation Error Cost Optimization of Demand Side Management Based on Massive MIMO Communication Network in Smart Grid

The estimation error cost (EEC) of smart grid under the demand side management is related to the network behavior and the accuracy of energy dispatch. In this paper, a communication and energy bidirectional link connection network (CEBCN) is proposed for decreasing the EEC of smart grid under demand side management. In the CEBCN, the network behavior is quantified to communication reliability and response validity. And the communication, implemented by massive multi-input-multi-output (MIMO), and energy dispatch can be realized, simultaneously. Also, the EEC is further decreased through a cost-priority based energy dispatch strategy proposed in this paper. Based on the CEBCN, the closed-form expressions are derived about the communication reliability and response validity. Naturally, the closed-form solution is derived for the EEC in the communication and energy transmission model, the results of which are used to investigate the EEC performance in smart grid. Numerical results validate that the reliability and validity of the proposed CEBCN and reveal the effect of various parameters on the EEC. Notably, the high EEC caused by communication instability can be reduced by at least 40% with the massive MIMO, and the cost-priority based energy dispatch strategy can further improve the EEC performance on communication infrastructure.

Energy Management Strategy in a Grid-Connected Microgrid Accomplishing an Energy Balance

Distributed generation has emerged as an alternative for energy production, which can combine clean and renewable energies with energies produced based on fossil resources. The microgrids allow to interconnect different type of resources, generation and storage systems, which imply to harmonize their characteristics to reach a good performance as a whole. The complexity to coordinate, synchronize and optimize the better operation conditions of the microgrids, require an energy management system (EMS) that decides the dispatch of energy, which meets the objective function, in addition to satisfying the marked restrictions and maintaining a balance between generation and consumption. In this work, an energy management system is proposed for a grid-connected microgrid that decides the dispatch and interaction of the equipment together, the energy dispatch suggested by the EMS gives the lowest operating prices and also satisfies the energy balance between generation and demand.

Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities

Grid-interactive communities are an emerging solution for optimal energy dispatching, improving grid stability, and enhancing the usage of on-site renewable energy by leveraging community flexibilities. This work introduces a novel Physically Consistent Deep Learning (PCDL)-based optimization framework for day-ahead energy dispatching and thermal comfort control within grid-interactive communities. Specifically, the PCDL model is developed to capture the thermal dynamics within the community while ensuring strict adherence to physics consistency. This consistency is defined with the aim of generating physically viable solutions in response to modified system inputs. Subsequently, the PCDL model is utilized for load estimation and indoor climate prediction within a proposed scheduling and control strategy: (1) an optimal energy dispatching plan is calculated based on the day-ahead grid market; (2) a real-time model predictive control (MPC) method is applied to minimize the utilization error and indoor comfort constraint violations caused by following the scheduled energy dispatching plan. A simulation case of a residential hall community at Cornell University with on-site renewable energy resources is implemented to demonstrate the effectiveness of the proposed approach. Comparative simulations, which include the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Gaussian Process (GP) regression models, confirm the performance advantage of capturing community thermal dynamic and control-oriented generalization ability when using the PCDL model. These results leads to notable improvements in indoor thermal comfort control, ranging between 65.4 and 68.7%, 63.6–79.3%, and 60.4–62.2% for each comparative model, respectively. Moreover, by harnessing community demand flexibility, we achieve energy cost savings between 29.5 and 39.7% compared to the baseline controller.

Assessing the value of hydrogen thermal energy storage and electric thermal energy storage in NEOM city

A large community such as NEOM City which is supposed to be powered only by wind and solar renewable energy requires a significant amount of energy storage. Given the limitations of the current energy storage technologies, from the total storable energy to the time this energy is stored, from the cost to the technology readiness level, it is important to provide assessments of continuing evolving alternative technologies. The cases of electric (external) thermal energy storage (eTES), and hydrogen thermal energy storage (hTES), are here considered. eTES is based on warming up a molten salt by using an electric resistance. The molten salt is then used to warm up a power cycle fluid for dispatchable energy production running a thermal power cycle. eTES suffers from round trip efficiency below 50 % but may handle a larger amount of energy at a lower cost compared to lithium-ion battery energy storage. eTES may benefit from integration with concentrated solar power with (internal) thermal energy storage. hTES is then based on green hydrogen production by electrolyzers, storage of the hydrogen in tanks, and combustion of the hydrogen to warm up a power cycle fluid for dispatchable energy production running a thermal power cycle. Of cost more difficult to estimate, also depending on the deployment of electrolyzers and round trip efficiencies below 50 %, hTES has the benefit to permit the storage of any amount of energy even over long time scales such as those needed to compensate for seasonal variability. Before electrolyzers provide the due amount of green hydrogen, the combustion power plant can be run with natural gas, delivering if not zero CO2 emission, certainly the lowest life cycle CO2 emission for dispatchable electricity at present. Finally, eTES and hTES can be coupled with each other, and also integrated with waste-to-energy (WTE) systems, for a more comprehensive and sustainable approach based on common thermal power cycles.

Research on Performance Evaluation Index System and Assessment Methods for Microgrid Operation in the Port Area

The vertical integration of ‘Generation-Grid-Load-Storage’ in microgrids for port areas is a prevailing trend. To comprehensively and accurately assess the operational efficiency of microgrids and develop an effective means for promoting the sustainable and scalable development of microgrids in port areas, an applicable evaluation index system and comprehensive evaluation method are essential. Addressing the issues of subjectivity in quantitative evaluation decision-making for different configuration and allocation schemes in port areas and the lack of a comprehensive evaluation system for microgrids in port areas, this study aims to scientifically and reasonably evaluate and select the most optimal configuration and energy dispatching schemes for microgrids in port areas. In this paper, the performance indicators of microgrids in port areas are hierarchically structured and classified into five dimensions: economic, energy efficiency, environmental, system reliability, and safety. A comprehensive evaluation index system for microgrid systems in port areas is constructed. Furthermore, an evaluation model for microgrid operational efficiency based on improved CRITIC-TOPSIS is proposed. The coefficient of variation is introduced to measure the relative strength and relative variation of indicators among dimensions, reducing the impact of correlation between indicators and improving the accuracy and objectivity of the evaluation results. Then, the TOPSIS method is used to calculate the comprehensive evaluation values and rank the optimal microgrid schemes in port areas. Finally, the feasibility of the evaluation index system and evaluation method for the operational efficiency of microgrids in port areas is verified through case analysis. The results indicate that the evaluation method based on improved CRITIC-TOPSIS can objectively and quantitatively evaluate the operational efficiency of microgrids in different port areas. The proposed method reasonably avoids the strong subjectivity in weight calculation by traditional expert judgment during the planning phase of microgrids in port areas, providing a new and scientifically effective engineering evaluation and analysis method for the evaluation of microgrid operational efficiency in port areas.

Low‐carbon economic optimization method for integrated energy systems based on life cycle assessment and carbon capture utilization technologies

AbstractIntegrated energy system (IES) is one of the ways to realize energy saving and emission reduction. Facing the crisis of fossil energy depletion and the challenge of global warming, low‐carbon technology and market economic guidance mechanism have an important impact on the low‐carbon operation of integrated energy system (IES). In this study, IES with carbon capture and utilization (CCU) technology is established, the carbon emission model based on life cycle theory is built for the input energy and energy system of IES, and the ladder carbon trading mechanism is considered in the market economy to establish the low‐carbon economic optimization model of IESs. A system combining carbon capture technology and power to gas can effectively consume new energy sources and provide energy dispatch flexibility. The ability to capture greenhouse gases generated by energy units reduces the system's carbon emissions. Based on the IES low carbon economy model, the model is analyzed with examples to analyze the correlation between different carbon trading prices and IES low carbon economy transport. We analyze the impact of CCU technology inputs on the low‐carbon nature of IES. The study proposes an IES dispatching model based on life cycle assessment and CCU technology to provide a new strategy for the operation of IESs and a new method for other energy systems to reduce low carbon emissions.

Efficient charging and discharging of a superconducting quantum battery through frequency-modulated driving

The quantum battery (QB), which can potentially store or dispatch energy more efficiently with quantum advantage, has attracted considerable attention lately in the field of quantum thermodynamics. With its quantum advantage, a QB could be charged more efficiently than the classical battery. In this paper, we theoretically and experimentally exploit the frequency-modulated stimulated Raman adiabatic passage (fmod-STIRAP) technique to improve the charging (discharging) efficiency of a cascaded three-level QB that is constituted by a superconducting transmon qutrit. The evolution of the qutrit and its thermodynamic properties are analyzed by carrying out the three-level quantum state tomography on the device. Our experimental results, which are confirmed by numerical simulations, show that the fmod-STIRAP technique yields remarkable advantages in population, ergotropy, and power in the charging (discharging) process.

An energy dispatch optimization for hybrid power ship system based on improved genetic algorithm

Due to the energy crisis and environmental deterioration, the emerging hybrid energy ship power system gradually replaced the traditional ship power system to keep environmental friendliness by employing the clean energy. However, the increase of energy storage and photovoltaic generation system brings enormous challenge to the optimization scheduling of hybrid energy ship power system. For this reason, an improved genetic algorithm-based optimal scheduling strategy for the hybrid energy ship power system is developed in this paper. Firstly, a novel hybrid energy ship power system model including the diesel generator, energy storage system, propulsion system, dynamic load and photovoltaic power generation device is constructed under the constraint of energy efficiency and greenhouse gases emissions. Considering the various navigation situations that the ship may encounter, such as photovoltaic power generation limit in extreme weather and diesel generator power change in load shedding, the corresponding scheduling optimization problems for the hybrid energy ship power system are established. Under the cost and gas emission constraints, an improved genetic algorithm-based scheduling optimization algorithm is proposed. By introducing the nonlinear parameter change model in crossover and mutation operator, the performance of improved genetic algorithm can be enhanced, such as convergence speed and global optimization ability. Compared with current works, the proposed scheduling optimization strategy can achieve the lowest cost while reducing environmental impacts. Finally, simulation results under the given navigation cases demonstrate the superiority of the proposed improved genetic algorithm-based scheduling optimization strategy.

Uncertainty-Aware Trading of Congestion and Imbalance Mitigation Services for Multi-DSO Local Flexibility Markets

The design of Local Flexibility Markets (LFMs) for energy and reserve dispatch of Renewable Distributed Energy Resources (RDERs) has recently been a topic of wide research. However, in an scenario with high penetration of RDERs deployed among different Distribution System Operators (DSOs) jurisdictions, further operational requirements concerning congestion and imbalance mitigation services may lay down. In this context, the relationship between capacity and energy products and the uncertainty management scheme becomes essential for procuring of RDERs flexibility. This paper proposes an uncertainty-aware Multi-DSO LFM. This market setting uses flexibility products to mitigate congestions and imbalances among different DSOs. First, capacity products hold back the flexibility of the RDERs in anticipation of contingencies. Then, energy products are activated within each time slot if the event finally occurs. LFM is solved in a coordinated and decentralised fashion using the properties of the Alternating Direction Method of Multipliers (ADMM), preserving participants' privacy. Uncertainty of RDERs and energy events duration are modelled using chance-constraint linear optimisation. The proposed methodology has been tested in a case study based on a realistic dataset and radial distribution systems.

Business models for local energy market and circular economy establishment in municipalities: A case study in an Austrian municipality

Energy communities often lack authority for establishment and operation management. Municipal authorities could take the role of such community operators. Therefore, Local Sustainable Municipalities are introduced, providing a local energy market on the municipal level with inclusion of sustainable resource utilization. The analyses include examinations of the scope of local markets in a municipality, portfolio investigations on different waste treatment plants and greywater system installation analyses. Furthermore, different adopted municipality strategies and their impact on municipal portfolio and market operation are examined. A clustering-based optimization framework for portfolio and market optimization is developed to perform these analyses. The proposed modeling approach leads to a significant model size reduction compared with hourly data optimization while providing location determination and portfolio estimation. The results indicate energy-sharing differences between municipal markets and local markets in energy communities. Decentralized energy provision is similar to centralized energy provision but on the municipal level. Furthermore, results show that waste incineration energy recovery can provide dispatchable low-emission energy with a high level of energy security and should be supported until the energy transition is more advanced. Finally, results on local strategies show that specific municipal goals always lead to increased costs for the municipality.

Geothermal energy exploitation in an island-based 100% renewables strategy. Case study of Tenerife (Spain)

The large-scale integration of non-dispatchable renewable energy must be carefully planned, especially in poorly robust electrical energy systems. Tenerife is the most populated of the islands in the Canary Archipelago (Spain) and has an isolated electrical system. It is envisaged for the horizon of 2040 that the island will have a high contribution of non-dispatchable renewable energies (fundamentally wind and solar photovoltaic). Tenerife also has exploitable high enthalpy geothermal resources. An energy planning model is implemented in this paper based on which different scenarios with high non-dispatchable renewable energy penetration in the island's electrical system are simulated. Geothermal energy is also incorporated in the simulations as a dispatchable renewable energy source, with the power required sized according to the hypotheses of each scenario. The results are compared according to different technical and economic parameters. An evaluation is also made of the influence that the capacity of the electrical system to take on higher non-dispatchable renewable power and the presence of energy accumulation systems might have on the minimization of losses in the non-dispatchable renewable energy capacity factor. The results of the simulated scenarios show that geothermal energy could meet up to 28.8% of the island's electrical energy demand, increasing the stability, flexibility and supply guarantee of system. A 100% renewable system could be possible through the incorporation of this energy source and the planning of energy accumulation systems. In the case study, the economic saving for Spain's electrical system could exceed 400 €million per year.

HelioCon: A roadmap for advanced heliostat technologies for concentrating solar power

Heliostat-based concentrating solar-thermal power (CSP) systems can offer immense potential to provide low-cost, dispatchable renewable thermal and electrical energy to help achieve 100% decarbonized energy infrastructure in the United States. Heliostats are a major determinant of both capital cost and performance of state-of-the-art commercial molten salt towers and Generation 3 CSP systems. In 2021, the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) launched the Heliostat Consortium (HelioCon), a five-year initiative to advance heliostat technologies. The HelioCon mission is threefold: (1) establish strategic core testing and modeling capabilities and infrastructure at national labs; (2) support heliostat technology development in relevant industries; and (3) serve as a central repository to integrate industry, academia, and other stakeholders for heliostat technology research, development, validation, and deployment. In this Perspective, HelioCon presents a roadmapping study on advancing heliostat technologies, intended as a central reference for the entire CSP community.

The systematic application of plant dyeing techniques based on sustainable energy systems in ecological textile design

Global energy demand is increasing, and non-renewable resources are increasingly scarce. The development and utilization of renewable resources are imperative. In order to enhance the environmental friendliness of ecological textile parks, the study proposes the systematic application of plant dyeing techniques based on sustainable energy systems in the design of ecological textiles. The experiment utilized resources such as solar energy and constructed a sustainable energy scheduling system for the dyeing and weaving park. The ratio of heat/cold, electricity, and hot water varies at different times throughout the day. Among them, the power supply ratio is municipal > photovoltaic > energy storage, and the photovoltaic power supply ratio accounts for about 33%. The daily planned power supply load is 50 KW, and the actual power supply load is approximately 38 KW. The energy dispatch system in the dyeing and weaving park has reduced the actual power supply load by about 24%. The corrected color matching error value of plant dye three color and four-color blends is less than 1.0, and the simulation data of the ratio has significantly improved to a certain extent. This indicates that the addition of a correction coefficient helps to correct the actual production ratio and reduce the impact of blended yarn color during the production process.