Dispatch Approach Research Articles

Energy management system for multi interconnected microgrids during grid connected and autonomous operation modes considering load management

This study focuses on improving power system grid performance and efficiency through the integration of distributed energy resources (DERs). The study proposes an artificial intelligence (AI) based effective approach for economic dispatch and load management for three linked microgrids (MGs) that operate in both grid-connected and autonomous modes. A day-ahead scheduling method is suggested to calculate the optimal set points for various energy sources in MGs considering various system constraints for safe operation. In addition, a load management approach that shifts the controllable loads from one interval to another is applied to reduce the operating cost of MG. To handle the optimization challenges of energy scheduling and load shifting such complexity and non-linearity, an advanced meta-heuristic method known as the one-to-one based optimizer (OOBO) is used. Overall, the paper proposes a viable and efficient methodology for economical distribution in linked microgrids, which takes advantage of renewable energy resources and incorporates scheduling optimization via the OOBO algorithm. The proposed energy management strategy enhances the system performance, increases energy efficiency, and reduces the daily operational cost by 1.6% for grid connected mode and by 0.47% for islanded operation mode.

A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators

AbstractThe consideration of mileage settlement in the frequency regulation market has encouraged fast‐acting units, such as converter‐interfaced generators (CIG) and electric vehicle stations, to actively participate in load‐generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid‐responding units. In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage‐based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.

Enhancing efficiency and economy of hydrogen-based integrated energy system: A green dispatch approach based on exergy analysis

Hydrogen-based integrated energy system holds promise for leveraging the low carbon advantage of hydrogen, but inefficiency of electrolysis hydrogen production leads to significant exergy loss within its operation. Addressing this, this paper focuses on the energy-saving and economic operation of the hydrogen-based integrated energy system, proposes a novel green dispatch approach using loss cost as the objective function and establishes a refined loss cost accounting model based on exergy analysis theory. Firstly, derived from the analogy of exergy loss, the concept and accounting framework of system loss cost are proposed. Secondly, employing exergy analysis and consistent unit exergy cost principle, equipment operation loss cost is calculated through energy, exergy flows analysis and cost allocation. Thirdly, utilizing the total of equipment operation loss cost, wind curtailment penalty cost, and transmission line loss cost as the objective function, a green dispatch model is formulated to minimize overall loss cost while ensuring energy balance and other constraints. Finally, simulations analysis are conducted. The results show that compared to economic dispatch, minimized exergy loss dispatch and multi-objective dispatch methods, proposed approach increases exergy efficiency by 0.54 %, reduces cost by 6.13 % and increases the scale of hydrogen utilization by 3.45 %, respectively.

A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks

Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and resource limitation constraints. The proposed real-time resource dispatch approach takes full advantage of the transferable characteristics for partial original plan tasks to adjust the pre-scheduled operation plan and release flexible resources for immediate processing of the burst task, completing burst tasks quickly and minimizing the impact for previous planned tasks on the edge computing device. The capability of the proposed method to efficiently deal with the burst tasks is also verified by the case study.

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm.

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model's effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

Maximizing profitability through cloud-enabled Reinforcement Learning for UAV coverage in real-time e-business applications

Real-time e-business applications are vital for operational efficiency, but connectivity challenges persist, particularly in remote or crowded areas. Drone Base Station (DBS) architecture, proposed for Beyond fifth Generation (B5G) and Sixth Generation (6G) multi-cell networks, offers on-demand hotspot coverage, addressing connectivity gaps in remote or crowded environments. DBSs provide a promising solution to meet the demanding requirements of high data rates, real-time responsiveness, low latency, and extended network coverage, particularly for real-time e-business applications. A critical challenge in this context involves efficiently allocating the needed number of DBSs to the different hotspot service areas, referred to as cells, to optimize the operator’s total profit under unpredictable user demands, varying area-specific service costs, and price dependence real-time e-service. The objective is to achieve the highest total revenue while minimizing the cost (cost savings) throughout the multi-cell system. This challenge is formulated as a profit-maximization discount return problem, integrating the coverage constraint, the variable cell-dependent operational cost, the e-service-based price and the uncertain demands of users across cells. Traditional optimization methods fail due to environmental uncertainty, which leads to the need to reformulate the problem as a Markov Decision Problem (MDP). We introduce a cloud-based Reinforcement Learning (RL) algorithm for DBS dispatch to address the MDP formulation. This algorithm dynamically adjusts to uncertain per-cell user distributions, considering variable operating costs and service-dependent prices across cells. Through extensive evaluation, the RL-based dispatch approach is compared with reference drone dispatch algorithms, demonstrating superior performance in maximizing operator profit through cost savings by optimizing DBS dispatch decisions based on learned user behaviors, variable operational costs, and e-service types.

A Distributed Learning-Based Economic Dispatch Approach of Microgrids Considering Operational Mode Switch

A Distributed Learning-Based Economic Dispatch Approach of Microgrids Considering Operational Mode Switch

FedPT-V2G: Security enhanced federated transformer learning for real-time V2G dispatch with non-IID data

The rising popularity of electric vehicles (EVs) underscores the potential of vehicle-to-grid (V2G) technology to contribute to load peak-shaving, valley-filling, and photovoltaic (PV) self-consumption. Effective V2G control strategies can be obtained by data-driven techniques, which is able to leverage historical and current data to inform future decision-making amidst uncertainties. However, the centralized collection and sharing of data among charging stations face challenges due to data asset concerns. Furthermore, even if data sharing hurdles are overcome, the non-independent and non-identically distributed (Non-IID) nature of data across charging stations can still negatively impact performance. In this study, we introduce FedPT-V2G, a security-enhanced federated transformer learning approach for real-time V2G dispatch that addresses Non-IID data. We employ deep learning models trained on historical and current data to enable real-time decision-making, facilitating both load shifting and PV self-consumption. Additionally, we utilize federated learning to jointly train a global model across all charging stations without collecting or sharing any local private data. We pioneer the application of the Proximal algorithm and Transformer model to tackle data distribution discrepancies within the V2G scheduling prediction task. The Proximal algorithm employs regularization techniques to align local models at each charging station more closely with the global model during updates. Concurrently, the multi-head attention mechanism within the Transformer model allows learned feature vectors to diverge, enabling better exploitation of variations across the entire feature space. Finally, we validate the proposed FedPT-V2G approach through extensive numerical simulations, demonstrating comparable performance to centralized learning on both balanced (98.93% vs 98.65%) and imbalanced (92.15% vs 92.20% in label skew) datasets.

Carbon-Energy Synergetic Energy Power System Economic Dispatch Method

Low carbon is a key driver for the future development of power systems. This paper proposes a carbon-energy synergetic approach for economic dispatch in energy power systems. Firstly, by integrating multi-energy conversion models and carbon emission flow theories, a coupled interactive mechanism model is constructed. Building upon this foundation, an economic optimization dispatch model for comprehensive energy power systems is introduced, leveraging the synergistic value of carbon energy. This method supports economic dispatch in comprehensive energy power systems, considering both security and economic requirements while reducing carbon emissions. This integrated approach offers a promising solution for achieving low-carbon operational objectives and supporting the transition to sustainable energy infrastructure. Typical case studies in this paper validate the effectiveness of the proposed method.

Optimal dispatch for cross-regional integrated energy system with renewable energy uncertainties: A unified spatial-temporal cooperative framework

Cross-regional energy sharing is a promising way to alleviate the imbalance in energy distribution. However, there is a risk that the uncertain fluctuations of intermittent renewable energy sources (RES) could be transmitted across regional transmissions, resulting in large-scale system economic inefficiency and security concerns. To this end, a unified spatial-temporal cooperative framework for the integrated energy system, which considers the coordination between intra-regional multi-energy coupling and cross-regional multi-agent-system energy sharing, is proposed in this paper. First, in the temporal dimension, a multi-time-scale stochastic decision-making model is established based on stochastic model predictive control (SMPC). Thus, slow-response units are arranged to gain economic goals in day-ahead stage and the closed-loop rolling adjustment is implemented on fast-response units to achieve accuracy supply-demand balance in real-time stage. Second, in the spatial dimension, a fully distributed dispatch approach is adopted to address the information sharing barriers, which only requires neighboring information and considers consistency of boundary control variables on border lines. Finally, to improve the computational efficiency, the established complex time sequential model is decomposed into a set of time-discrete parallel sub-problems, using the optimal condition decomposition (OCD) technique. Simulation results on a cross-regional test system demonstrate that proposed framework can increase the wind power consumption capacity and reduce operation cost through cross-regional cooperation, while the parallel decomposition can save 67.25 % of the computation time relative to the serial computation.

A distributionally robust optimization approach for optimal load dispatch of energy hub considering multiple energy storage units and demand response programs

The coupling relationship among various energy sources has been consistently reinforced by the advancement of the energy Internet. In the context of modeling multi-energy systems, the energy hub (EH) represents a significant and valuable approach. To attain optimal operation of the EH, this study proposes an optimal load dispatch model for a system that incorporates a combined heat and power (CHP) unit, gas boiler (GB), electric chiller (EC), absorption chiller (AC), heat energy storage (HES) unit, and electrical energy storage (EES) unit. A two-stage distributionally robust optimization (DRO) method that is driven by data is employed to address the uncertainties of electricity prices. Additionally, the proposed two-stage DRO model is effectively solved through the utilization of the column and constraint generation (C&CG) algorithm. Three cases are discussed in the paper with various energy storage units and demand response (DR) settings. The simulation results show that by deploying energy storage units and participating in DR projects, the EH system can reduce the total costs by 2.56 % and 10 %, respectively. Meanwhile, simulation results reveal that the proposed data-driven DRO model can achieve a compromise between the economy and robustness of the scheduling model compared with stochastic optimization (SO) and robust optimization (RO) methods. The simulation results illustrate the effectiveness of the proposed model for ensuring the economical and efficient operation of the system in the face of electricity price uncertainties.

Optimal dispatch approach for rural multi-energy supply systems considering virtual energy storage

In response to the underutilization of energy and insufficient flexible operation capability of rural energy supply systems in China, this study proposes an optimal dispatch approach for a rural multi-energy supply system (RMESS) considering virtual energy storage (VES). First, to enable the flexible utilization of rural biomass resources and the thermal inertia of residential building envelopes, this study constructed VES-I and VES-II models that describe electrical-thermal and electrical-gas coupling from an electrical viewpoint. Subsequently, an RMESS model encompassing these two types of VES was formulated. This model delineates the intricate interplay of multi-energy components within the RMESS framework and facilitates the precise assessment of the adjustable potential for optimizing RMESS operations. Based on the above models, a day-ahead optimal dispatch model for an RMESS considering a VES is proposed to achieve optimal economic performance while ensuring efficient energy allocation. Comparative simulations validated the effectiveness of the VES modeling and the day-ahead optimal dispatch approach for the RMESS.

A novel distributed approach for event-triggered economic dispatch of energy hubs under ramp-rate limits integrated with sustainable energy networks

This paper investigates a new consensus-oriented distributed approach for the event-triggered (ET) economic dispatch problem (EDP) over a smart grid under ramp-rate limits (RRLs) integrated with green power sources (GPSs) such as solar and wind energy for demand response strategies over hybrid energy power systems. To address the RRL condition, the authors have transformed the RRLs as minimum and maximum bounds on the derivative of generation for a generator. Then, a Karush–Kuhn–Tucker (KKT) condition and a more practical approximate KKT condition are developed for determining the optimality conditions. A practical ET protocol is proposed over a topology between generators by application of the proposed approximate KKT condition. In contrast to existing distributed optimization methods, this paper provides both optimally condition and distributed optimization scheme for dealing with RRLs integrated with sustainable hybrid energy systems. In addition, a computationally efficient ET mechanism, eliminating Zeno behaviour, has been considered for dealing with the efficient utilization of communication resources. This study incorporates the real-time input data from thermal production plants and GPSs for experimental analysis. RETScreen software having data-set of over 6,700 local meteorological stations is applied to obtain input data for GPSs. Furthermore, Weibull and Beta distribution functions have been applied for dealing with uncertainty in wind and solar energy sources. Two case studies are examined (with and without GPSs), and simulation results demonstrated the suitable performance of the proposed distributed ET EDP approach.

Energy efficiency of the Visitor Pattern: contrasting Java and C++ implementations

Design patterns are applied frequently during software evolution in order to make the software more flexible and amenable to extension. One little-studied aspect of design patterns is their propensity to increase run-time energy consumption due to the indirection and additional structure they introduce. In this paper we study the impact of the Visitor pattern on energy efficiency. The Visitor pattern separates an algorithm from the objects it acts upon and improves maintainability by placing each algorithm within a single visitor class. This is at the cost of increased indirection due to the double dispatch required when the algorithm is invoked. We experimentally investigate the energy impact of varying the implementation of this pattern, and of removing the pattern entirely from software written in Java and C++. In our results we observe energy consumption reductions greater than 7% in a Java-based textbook example when the pattern is implemented using reflective dispatch, and reductions of over 10% when experimenting with an open source Java project, JavaParser. The complete removal of the pattern yields more complex results, with little impact in the textbook example but reductions of over 7% in the JavaParser study. To explore the generalisability of our findings, we subsequently apply the same transformations to the C++ based CppParser. Total pattern removal here sees energy consumption reductions of over 66% while the reflective dispatch approach increases energy consumption by up to 2012%. Our results highlight the energy savings that can be achieved when the Visitor pattern is removed both in Java and C++ implementations, and also show that some language specific features can allow for further energy savings when the implementation of the pattern is varied.

A multistage distributionally robust optimization approach for generation dispatch with demand response under endogenous and exogenous uncertainties

AbstractDecision‐dependent (endogenous) uncertainties (DDUs), as a new type of uncertainties revealed recently, couple dispatch decisions with uncertainty parameters and thus render power system dispatch more challenging. However, most previous works handled various DDUs via stochastic programming (SP) or robust optimization (RO) in a two‐stage framework, which undoubtedly introduces the drawbacks of SP and RO, and cannot meet the nonanticipativity requirements in power scheduling. In this paper, a multistage distributionally robust optimization (DRO) method for generation dispatch with demand response (DR) is proposed considering the DDUs of deferrable loads and the decision‐independent (exogenous) uncertainties (DIUs) of wind power and regular loads. By analyzing the structure of decision‐dependency parameters, a novel data‐driven decision‐dependent ambiguity set is proposed, which provides a generic framework for formulating DDUs and DIUs simultaneously. Then a multistage DRO model with nested max‐min structure is developed to integrate the merits of DRO and nonanticipativity into generation dispatch. The proposed model is solved by tailored reformulation method and improved stochastic dual dynamic integer programming (SDDiP). Case studies illustrate the effectiveness of the proposed approach by comparing with the multistage SP, RO, and decision‐independent DRO methods.

Robust post-disaster repair crew dispatch for distribution systems considering the uncertainty of switches

After natural disasters’ strike, the remote-controlled switch plays a key role in network reconfiguration to restore the critical load. However, the status of these switches is hardly acquired in case of cyber malfunctions. This paper proposes a robust repair crew dispatch approach for distribution systems considering the uncertainty of remote-controlled switches. The proposed approach is formulated as a tri-level robust optimization model to obtain the repair crew dispatch strategy applicable to possible fault scenarios. The system operator determines the repair crew dispatch scheme at the first level. At the second level, the real-time status of switches under different combinations of faults and repair processes is modeled and the worst fault scenario is calculated. Based on the real-time status of switches, the operator utilizes distributed generators and network reconfiguration to restore loads. A nested column-and–constraint generation (NC&CG) algorithm is customized for the proposed robust model to achieve the optimal solution. Simulation results on the IEEE-33 node distribution test system and the IEEE 123-node system show the proposed approach can effectively reduce the maximum system loss and the NC&CG algorithm can significantly reduce the computing time.

Design and implementation of an off-grid hybrid microgrid for Chittagong and Faridpur

The challenge of rural electrification has become more challenging today than ever before. Grid-connected and off-grid microgrid systems are playing a very important role in this problem. Examining each component’s ideal size, facility system reactions, and other microgrid analyses, this paper proposes the design and implementation of an off-grid hybrid microgrid in Chittagong and Faridpur with various load dispatch strategies. The hybrid microgrids with a load of 23.31 kW and the following five dispatch algorithms have been optimized: (i) load following, (ii) HOMER predictive, (iii) combined dispatch, (iv) generator order, and (v) cycle charging dispatch approach. The proposed microgrids have been optimized to reduce the net present cost, CO2 emission, and levelized cost of energy. All five dispatch strategies for the two microgrids have been analyzed in HOMER Pro. Power system reactions and feasibility analyses of microgrids have been performed using ETAP simulation software. For both the considered locations, the results propound that load following is the outperforming approach having the lowest energy cost of $0.1728/kWh, operational cost of $2944.13, present cost of $127,528.10, and CO2 emission of 2746 kg/year for the Chittagong microgrid and lowest energy cost of $0.2030/kWh, operating cost of $3530.34, present cost of 149,287.30, and CO2 emission of 3256 kg/year for Faridpur microgrid with a steady reaction of the power system.

Enhanced Economic Load Dispatch by Teaching–Learning-Based Optimization (TLBO) on Thermal Units: A Comparative Study with Different Plug-in Electric Vehicle (PEV) Charging Strategies

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching–Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm was utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization was investigated, and different charging profiles and probability distributions were defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources were introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios.

Chance-Constrained Generic Energy Storage Operations Under Decision-Dependent Uncertainty

Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations. While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. Simulation-based analysis shows that the proposed methods yield conservative, but credible, GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units.

Real-time load dispatch in hydropower plant based on D3QN-PER

The conventional in-plant load dispatch approach has limited decision-making capacity and basic objectives. Small and medium-sized hydropower plants are challenged with the issues of meeting the water dispatch requirements and ensuring the decision-making accuracy and efficiency at the same time as the intensity of peak and frequency modulation of new power systems increases. To match the unit operation strategy and the water dispatch requirements of the power plant, an in-plant load dispatch model that takes into account the needs of both power generation dispatch and water dispatch was proposed in this paper. This was done to address the challenge of meeting the water dispatch requirements. A reinforcement learning approach was used to increase the computation efficiency of optimal load dispatch to address the challenge of ensuring decision-making accuracy and efficiency for real-time load dispatch schemes. According to the application findings at the Shenxigou Hydropower Plant (SXG), (1) this model was built to complete the work of peak and frequency modulation of the power grid while also taking into account the water dispatch requirements of small and medium-sized hydropower plants. With the minimum water usage as the water dispatch objective, this model was able to reduce the calculated water usage for power generation by 1.74 × 106m3 in comparison to the load dispatch table method. The calculated cumulative water level deviation of the model was reduced by 65.14 m when the water dispatch target was water level control. (2) The correctness and effectiveness of the decision-making process could be taken into account by this model. This model could meet the requirements for real-time dispatch decision-making after enhancing load discrete accuracy and adding roll-up computation of forecast information, and its calculation times were 9.46% and 15.10% of those of the load dispatch table technique, respectively.