Dispatch Algorithm Research Articles

Unified dispatch of grid-connected and islanded microgrids

This work develops microgrid dispatch algorithms with a unified approach to model predictive control (MPC) to (a) operate in grid-connected mode to minimize total operational cost, (b) operate in islanded mode to maximize resilience during a utility outage, and (c) utilize weighting factors in the grid-connected objective function to preserve islanded capability (on-site fuel reserves, battery state of charge) to enhance resilience in the potential event of an unplanned grid outage. Resilience is defined using microgrid survivability (probability to serve 100% of critical load), autonomy (duration of time to serve 100% of critical load), and unserved energy (curtailed critical load) for a target of 7 days during a grid outage. The developed methods are applied to a military microgrid with 2,250 kW of diesel generation, 3,450 kW/13,800 kWh battery storage, and 16,479 kW of solar photovoltaics. Sensitivity analysis is conducted to determine the selection of weighting factors to have the best impact on three developed objectives: grid-connected economics, islanded resilience, and carbon intensity. Optimal weighting factors reduce operating costs by 0.1%, increase survivability by 3.9%, increase autonomy by 16.7%, reduce unserved energy by 94.1%, and increase carbon intensity by 2.5%.

Adaptive salp swarm algorithm for sustainable economic and environmental dispatch under renewable energy sources

Many developing nations face energy crises, in addition to the global warming issue, owing to the recent increase in oil prices, accordingly, identifying the alternate energy sources. As a result, scientists around the globe are investigating new computational techniques for the energy dispatch under hybrid power systems. The aim of this research is to explore the integration of green energy sources (GESs) such as wind and solar in the conventional hydro–thermal coordination problem (CHTCP) to reduce the energy production cost along with the environmental benefits. The proposed hybrid energy coordination problem considers a probabilistic model for incorporating GESs uncertainties by using the point-estimation technique. Weibull and Beta distribution functions are utilized for the treatment of uncertain input variables of wind and solar sources, and the overall energy production cost is optimized via an improved heuristic swarm-based paradigm, namely, adaptive salp swam algorithm (ASSA). Three complex test systems are chosen (with and without GESs) to demonstrate the effectiveness of ASSA on hybrid power systems. The control parameters of ASSA are modified to maintain a balance between the exploration and exploitation phases to improve the convergence and to achieve better solution. The simulation results indicate that the integration of GESs into CHTCP has lowered the operational expenses by 10 percent and emissions by 64 percent. The findings have been compared with the existing techniques to show the effectiveness of the proposed approach.

A Fully Distributed ADP Algorithm for Real-Time Economic Dispatch of Microgrid

This paper proposes a fully distributed approximate dynamic programming (FD-ADP) algorithm framework for real-time economic dispatch of a microgrid (MG). Firstly, a modified distributed consensus-based algorithm (DCBA) framework with power restriction factors is proposed to distributedly obtain the real-time ED decision of MG containing distributed energy resources with linear cost functions, which improves the convergence stability compared with the traditional DCBA framework. Then, combined with the approximate dynamic programming (ADP) theory, a FD-ADP algorithm is proposed to deal with data uncertainties. To this end, the piece-wise linear (PWL) function is applied to approximate the value function, and a distributed slope update approach is proposed to embed empirical knowledge into the value function while ensuring the distributed property in off-line training. With the well-trained PWL function assisting in decision, the distributed real-time ED strategy of MG with approximate global optimality is obtained by the proposed FD-ADP algorithm during the entire schedule horizon (e.g., 24 hours). Finally, case studies on the modified IEEE 6-bus MG system and IEEE 123-bus MG system demonstrate the superiority of solution convergence, validity of distributed training, and optimality of distributed schedule of the proposed FD-ADP algorithm.

A fully distributed algorithm for dynamic economic dispatch of cross regional power systems based on alternating direction multiplier method

n this study, a novel approach to dynamic economic dispatch for multi-region power systems is introduced, leveraging the alternating direction multiplier method (ADMM) for computational efficiency. The method begins by dividing an interconnected power system into coherent groups, assigning local processors to each area to estimate black-box transfer function models using Lagrange multipliers. These processors then collaborate to form a global transfer function model through a consensus algorithm, leading to the derivation of a transfer function residue for the optimal wide-area control loop. A wide-area damping controller is designed from this residue, and its effectiveness is validated on two area and IEEE-39 bus test systems using RTDS/RSCAD and MATLAB co-simulation platforms. Simultaneously, the study proposes an economic scheduling model that aims to minimize operational costs while meeting various operational constraints. By severing inter-regional connections, the ADMM enables distributed resolution of the optimization problem, breaking it down into regional sub-problems that are iteratively solved to reach the global optimum. This process eliminates the need for a centralized data repository for multiplier updates, supporting a fully distributed scheduling approach. Additionally, a multi-period optimization technique is integrated to address the power system’s temporal dependencies. The approach's validity is demonstrated through empirical analysis of a tri-regional interconnected system based on the IEEE standard test system, confirming the strategy's effectiveness in economic dispatch.

A chaotic Jaya algorithm for environmental economic dispatch incorporating wind and solar power

<abstract> <p>The integration of renewable energy resources (RESs) into the existing power grid is an effective approach to reducing harmful emission content. Environmental economic dispatch is one of the complex constrained optimization problems of power systems. These problems have become more complex as a result of integrating RESs, as the availability of solar and wind power is stochastic in nature. To obtain the solution of such types of complex constrained optimization problems, a robust optimization method is required. Literature shows that chaotic maps help to boost the search capability through improvisation in the exploration and exploitation phases of an algorithm; hence, they are able to provide superior solutions during optimization. Therefore, in this study, a new optimization technique was developed based on the Jaya algorithm called the chaotic Jaya algorithm. Here the main aim was to investigate the impact of RES integration into conventional thermal systems on total power generation cost and emissions released to the environment. The proposed approach was tested for two standard cases: (i) scheduling of a committed generating unit for a specific time and (ii) scheduling of a committed generating unit for a time period of 24 hours with 24 intervals of 1 hour each. The simulation results show that a tent map is the best-performing map for a sample problem under consideration, as it provides better results. Hence, it has been considered for detailed analysis.</p> </abstract>

Distributed Privacy-Preserving Algorithm for Economic Dispatch and Demand Response of Smart Grid With Homomorphic Encryption

Distributed Privacy-Preserving Algorithm for Economic Dispatch and Demand Response of Smart Grid With Homomorphic Encryption

Byzantine-Resilient Distributed Algorithm for Economic Dispatch: A Trust-Based Weight Allocation Mechanism

Byzantine-Resilient Distributed Algorithm for Economic Dispatch: A Trust-Based Weight Allocation Mechanism

Optimization Study of Carbon Emissions in Wind Power Integrated Systems Based on Optimal Dispatch Algorithm

Abstract With the integration of wind power into the power system, dispatch becomes more complex and existing algorithms are no longer applicable. This paper focuses on optimizing carbon emissions in wind farm generation while considering issues related to wind power integration and carbon trading. An optimal dispatch algorithm was designed with the objective of minimizing total costs, which was then solved using the cuckoo search (CS) algorithm. Additionally, an adaptive improvement was made to the CS algorithm to obtain the improved cuckoo search (ICS) algorithm. An analysis was conducted on a case study with 10 units. The ICS algorithm obtained higher quality solutions, with a total cost of $ 632 719 and a calculation time of 0.51 minutes, which was superior to the solutions obtained by the particle swarm optimization and CS algorithms. Fluctuations in the confidence level of system rotation reserve capacity could lead to variations in the final system cost, which needs to be adjusted according to actual conditions. The dispatch scheme obtained by the ICS algorithm showed reduced carbon emissions, total costs, and better performance when compared with the optimal dispatch algorithm in different scenarios. The results show that the proposed methods are reliable and practical.

Driving Sustainable Energy Dispatch for Remote Communities Using Levy Flight Salp Swarm Algorithm

This study evaluates three energy dispatch algorithms to find the best way to improve hybrid energy systems in rural areas: load following (LF), cycle charging (CC), and a novel customized strategy (CS). The study examined solar and wind resources as well as projected patterns of energy consumption with a particular focus on Bambur village in Taraba State, Nigeria. The Levy Flight Salp Swarm Algorithm (LFSSA) was used in the study to assess system configurations for each method, accounting for battery storage, wind turbines, diesel generators, and photovoltaic (PV) arrays. The most cost-effective and efficient approach was determined to be the Customized Strategy (CS), which produced the lowest Net Present Cost (NPC) of 0.119/kWh. The Load Following (LF) technique had the highest costs at 0.134/kWh, while the Cycle Charging (CC) method had intermediate costs with an NPC of 0.127/kWh. With a 580-kWh battery bank, a 10 kW wind turbine, 332 kW of PV capacity, and a 78 kW diesel generator, the CS approach showed better component sizing balance. The trade-offs between energy output, storage, and backup power in real-time were optimized using this design. Sensitivity analysis revealed that increasing interest rates from 10% to 18% led to a rise in LCOE, while diesel cost fluctuations showed a non-monotonic impact on LCOE, peaking at $0.79/L before declining due to increased reliance on renewable sources and storage. CS strategy’s balance of investment and efficiency makes it ideal for remote energy management, offering key insights for rural electrification.

Distributed Economic Dispatch Algorithm With Quantized Communication Mechanism

Distributed Economic Dispatch Algorithm With Quantized Communication Mechanism

An Automatic Cascaded Movement Approach to Solve the Energy Replenishment Problem in WPT-Based Mobile WRSNs

In Wireless Rechargeable Sensor Networks (WRSNs), prolonging the sensors’ lifetime is a crucial issue that requires attention. Most studies in the field have focused on path planning for mobile chargers to replenish energy supply for static sensors. Different from the most existing studies, this paper explores the energy replenishment problem in WRSNs with mobile sensors. The paper addresses two issues to extend the network’s lifetime: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1$</tex-math> </inline-formula> ) how to balance energy consumption between mobile sensors using their mobility capabilities; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2$</tex-math> </inline-formula> ) how to dispatch redundant mobile sensors, charged and calibrated, to replace low-energy sensors. For the first issue, an energy balancing algorithm is proposed that uses cascaded movement to better the cascading schedule. For the second issue, a redundant mobile sensor dispatch algorithm is proposed that prioritizes mobile sensors most in need of energy replenishment for replacement by a charged and calibrated redundant mobile sensor. The proposed algorithms are extensively evaluated through simulation and have demonstrated their effectiveness in achieving energy balance and prolonging the network lifetime. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial automation applications often involve harsh and hazardous environments with high temperatures, dirt, dust, corrosive materials, and chemical risks. To ensure the longevity of sensors in such conditions, in addition to considering charging, sensors must also be regularly maintained and calibrated for accurate data sensing. However, maintaining and calibrating sensors requires the use of precise instruments that cannot be handled by mobile chargers. To address this issue, we propose studying how mobile sensors can cooperate with each other to complete monitoring tasks and return to the base station for charging, maintenance, and calibration before they run out of energy. The proposed algorithms are applicable to real-life precision monitoring applications in harsh or hazardous environments. This paper provides pseudo code, mathematical expressions, performance evaluation, and results, which can serve as a practical implementation guide for practitioners.

Distributed economic dispatch algorithm in smart grid based on event-triggered and fixed-time consensus methods

In this paper, a distributed economic dispatch algorithm based on event-triggered and fixed-time consensus is proposed for optimal dispatching among generators in smart grid. Aiming to quickly achieve economic dispatch in distributed smart grid and significantly reduce the frequency of communication between generators, the algorithm can address the supply–demand balance between the generation side and the consumption side, as well as the capacity constraints of the generators. Different from the relevant work, the proposed incremental cost can converge in fixed-time and the settling time is independent of the initial state of the generators. It will provide more flexibility for the system. Meanwhile, a fully distributed event-triggered strategy is adopted, which can reduce the interactive information between generators and avoid the waste of communication resources. Furthermore, the proposed algorithm is robust to the time-varying communication network topology. Finally, several examples are presented that validate the effectiveness of the algorithm.

Modeling an Agricultural Microgrid using pvlib-python: A Case Study in Bangladesh

The article focuses on sizing and designing microgrids with pvlib-python and the Python programming language. Pvlib-python is a free and open-source program for simulating solar photovoltaic (PV) systems. For the design, an existing case study of an agricultural microgrid comprised of PV arrays, batteries, and a biogas-based generator in an off-grid configuration was explored. The solar resources and PV system were modeled using pvlib-python, while the rest of the microgrid was built and simulated using a custom dispatch algorithm written in Python. The study also discussed an in-depth strategy for modeling PV utilizing various data sources using the included modules and functions. A similarly specified microgrid was also modeled in Homer Pro software and the results from the designed microgrid in Python were compared. The hourly distribution of data for both tools exhibits a noticeable deviation. The daily and annual distribution of most of the parameters, on the other hand, produce comparable results.

Multimodal hierarchical distributed multi-objective moth intelligence algorithm for economic dispatch of power systems

The issue of economic dispatch in the context of environmental sustainability is more challenging with the integration of clean energy into the grid and the increase in the size of power systems. Power engineers have a responsibility to seek a novel algorithm that can hierarchically simplify the problem, enhance the diversity of solutions, balance economic and environmental goals, reduce the environmental footprint, and increase economic efficiency. In this paper, a multimodal hierarchical distributed multi-objective moth intelligence algorithm is proposed, combines the hierarchical distributed method with the moth algorithm, introduces the loose equivalence of multimodality, and adopts the Pareto curve partition processing and the extraction of objective points. The proposed method converts the information exchange of the adjacent regions of each layer and the optimization of each layer to be performed in the bottom layer, achieving the coordinated optimization of the whole system through the parallel optimization of the bottom layer regions. The simulation results show that: (1) the computational efficiency is improved. In the 118-bus system, the proposed algorithm reduces 0.13% cost and 5.76% carbon emission than distributed optimization; the proposed algorithm reduces 0.53% cost and 3.81% carbon emission than centralized optimization; (2) the speed of computation is accelerated. In the 1139-bus system, three-layer distributed optimization and two-layer distributed optimization of the proposed algorithm reduce the time by 62.64% and 37.22% than distributed optimization, respectively; (3) the performance metrics of the proposed method demonstrate superiority.

Performance and Techno-Economic Analysis of Optimal Hybrid Renewable Energy Systems for the Mining Industry in South Africa

This paper presents an exploration of the potential of hybrid renewable energy systems (HRESs), combining floating solar photovoltaics (FPV), wind turbines, and vanadium redox flow (VRF) battery energy storage systems (BESSs) to expedite the transition from conventional to renewable energy for the mining sector in South Africa. The feasibility study assesses how to enhance the overall efficiency and minimize greenhouse gas emissions from an economic standpoint by using the Hybrid Optimization of Multiple Energy Resources (HOMER) grid software version 1.11.1 and PVsyst version 7.4. Furthermore, the BESS Covariance Matrix Adaptation Evolution Strategy (CMA-ES) dispatch algorithm is proposed to make the most of the battery storage capacity and capability, aligning it with the dynamic energy demand and supply patterns of an HRES. The proposed HRES includes a highly efficient SFPV with a performance ratio of 0.855 and an annual energy production of 15,835 MWh; a wind turbine (WT) operating for 2977 h annually, achieving a 25% wind penetration rate; and a dynamic VRF-BESS with a 15,439 kWh life throughput and a 3 s dispatch response time. This HRES has a CapEx of R172 million, a 23.5% Internal Rate of Return (IRR), and an investment payback period of 4.9 years. It offers a low Levelized Cost of Energy (LCoE) at 4.27 R/kWh, a competitive Blended Cost of Energy (BCoE) at 1.91 R/kWh, and a positive net present cost (NPC), making it economically advantageous without external subsidies. Moreover, it annually reduces CO2 emissions by 1,715,468 kg, SO2 emissions by 7437 kg, and NOx emissions by 3637 kg, contributing to a significant environmental benefit.

Integrating reinforcement-learning-based vehicle dispatch algorithm into agent-based modeling of autonomous taxis

Integrating reinforcement-learning-based vehicle dispatch algorithm into agent-based modeling of autonomous taxis

Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation.

Smartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers' availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched. This study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert. We developed and compared 4 analytical models to predict VFRs' response behaviors based on emergency event characteristics, volunteers' demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder's previous behavior. The highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs' demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs' previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy. VFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.

Reactive Power Dispatch Algorithm for a Reduction in Power Losses in Offshore Wind Farms

This paper presents a groundbreaking power distribution technique that focuses on the loss rate of individual wind turbines. Distinct from conventional methods, our strategy prioritizes seamless integration and adaptability within wind farm management systems. By evaluating power losses in specific branches of a wind farm, our approach enhances overall performance by strategically allocating reactive power to reduce cumulative losses. When compared to traditional uniform distribution and Particle Swarm Optimization (PSO) methods, our innovative approach stands out for its superior efficiency and adaptability. Comprehensive simulations underline the strengths and weaknesses of prevailing methods and underscore the superior efficacy of our proposed technique.

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.

The variables predictive of ambulance non-conveyance of patients in the Western Cape, South Africa

IntroductionEmergency medical service (EMS) resources are limited and should be reserved for incidents of appropriate acuity. Over-triage in dispatching of EMS resources is a global problem. Analysing patients that are not transported to hospital is valuable in contributing to decision-making models/algorithms to better inform dispatching of resources. The aim is to determine variables associated with patients receiving an emergency response but result in non-conveyance to hospital. MethodsA retrospective cross-sectional study was performed on data for the period October 2018 to September 2019. EMS records were reviewed for instances where a patient received an emergency response but the patient was not transported to hospital. Data were subjected to univariate and multivariate regression analysis to determine variables predictive of non-transport to hospital. ResultsA total of 245 954 responses were analysed, 240 730 (97.88 %) were patients that were transported to hospital and 5 224 (2.12 %) were not transported. Of all patients that received an emergency response, 203 450 (82.72 %) patients did not receive any medical interventions. Notable variables predictive of non-transport were green (OR 4.33 (95 % CI: 3.55–5.28; p<0.01)) and yellow on-scene (OR 1.95 (95 % CI: 1.60–2.37; p<0.01).Incident types most predictive of non-transport were electrocutions (OR 4.55 (95 % CI: 1.36–15.23; p=0.014)), diabetes (OR 2.978 (95 % CI: 2.10–3.68; p<0.01)), motor vehicle accidents (OR 1.92 (95 % CI: 1.51–2.43; p<0.01)), and unresponsive patients (OR 1.98 (95 % CI: 1.54–2.55; p<0.01)). The highest treatment predictors for non-transport of patients were nebulisation (OR 1.45 (95 % CI: 1.21–1.74; p<0.01)) and the administration of glucose (OR 4.47 (95 % CI: 3.11–6.41; p<0.01)). ConclusionThis study provided factors that predict ambulance non-conveyance to hospital. The prediction of patients not transported to hospital may aid in the development of dispatch algorithms that reduce over-triage of patients, on-scene discharge protocols, and treat and refer guidelines in EMS.