Total System Cost Research Articles

Optimal Sizing, Energy Balance, Load Management and Performance Analysis of a Hybrid Renewable Energy System

This work utilizes the particle swarm optimization (PSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two-step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained are then compared with results from the Non-dominant Sorting Genetic Algorithm II (NGSA-II), hybrid genetic algorithm–particle swarm optimization (GA-PSO) and the proprietary derivative-free optimization algorithm. An energy management system monitors the energy balance and ensures that the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consists of solar panels (151), wind turbine (3), inverter (122) and batteries (31). This results in a minimized TEC, TAC and LCOE of USD 469,200, USD 297,100 and 0.007/kWh, respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient conditions.

Metaheuristics-based multistage iterative optimization framework for decomposed high-speed rail systems planning problem

High-speed rail (HSR) systems planning and decision-making is a large-scale, multistage, intricate problem, where each stage represents a complex sub-problem. The solution approach to the multistage problem needs information exchange across the stages. A non-optimal planning and decision at any stage could yield a sub-optimal HSR system. This study proposes a novel multistage iterative HSR station location and alignment optimization framework (HSLAOF) to solve the main problem by decomposing it into the identification of (a) HSR potential regions and corridor, (b) station location, (c) shortest sequence/corridor of stations, and (d) collision-free alignment (horizontal and vertical) sub-problems. It integrates these sub-problems, which are solved separately using customized artificial intelligence- and motion planning-based metaheuristics such as particle swarm optimization for station location, ant colony optimization for shortest sequence of stations, path planner method for horizontal alignment and exploration, and exploitation-based ant colony optimization for vertical profile. In HSLAOF, the information from the upper and lower stages of the optimization process are exchanged and combined for a holistic optimized HSR system. Application of HSLAOF in the Mumbai-Ahmedabad HSR project yielded a solution that has 14.24 % lower total system cost, no environmental impact, 5.82 % better socio-economic impact, 21.14 % lesser noise and vibration impacted population, and 2.79 % higher station location utility than the one proposed by the experts. For predefined station location, the HSLAOF yielded alignment with 13.90 % lower total system cost and 24.00 % less noise and vibration impacted population than the one developed by experts. It required about 150 min to obtain a solution for each iteration.

Cost-effective reliability level in 100% renewables-based standalone microgrids considering investment and expected energy not served costs

Loss of load probability (LOLP) and expected energy not served (EENS) are commonly used in electrical power systems to evaluate reliability. LOLP defined as the probability that available generation capacity will be inadequate to supply customer demand. EENS defined as the expected amount of energy not being served to consumers by the system during the period considered due to system capacity shortages or unexpected power outages. Loss of Load Frequency (LOLF) is referred to a number of loss of load (LOL) event happened in the operation life span of the SMG. Loss of Load Reduction (LOLR) is defined as the required reduction in LOLF to obtain a specific reliability level. While power systems are designed to minimize LOLP and EENS, this is constrained by the total cost: investment cost, operation and maintenance cost, and cost of customer interruption (CCI). This research considers Standalone Microgrid (SMG), also known as Autonomous Microgrid which only operates in off-grid mode and cannot be connected to wider electrical power system. When designing a 100 % renewable energy integrated SMGs, it is crucial to determine the cost-effective reliability level (CERL). The CERL occurs when the total cost is minimum. This research proposes an approach to calculate the CERL for a fully renewable SMG. An analytical formulation is proposed to represent the LOLR needed to obtain a specific reliability level as a function of the required size of reliability improvement alternatives. The CCI is evaluated using LOLF and EENS indices. Finally, the total cost of the SMG system is evaluated for each reliability level. Consequently, the total cost of the SMG system is expressed as a function of reliability levels, and the minimum value of total cost and the corresponding reliability level are evaluated. In this research, a Monte Carlo Simulation (MCS) approach is used to find hourly LOLF, considering 25 years (219,000 h) of SMG lifespan, regression analysis is used for an analytical formulation, and mixed integer linear programming (MILP) is used for the investment decision making based on a cost minimisation approach. The result demonstrates that the CERL of the SMG system evaluated in the case study is 98.71 %.

ERTH scheduler: enhanced red-tailed hawk algorithm for multi-cost optimization in cloud task scheduling

Effective task scheduling has become the key to optimizing resource allocation, reducing operation costs, and enhancing the user experience. The complexity and dynamics of cloud computing environments require task scheduling algorithms that can flexibly respond to multiple computing demands and changing resource states. Therefore, we propose an enhanced Red-tailed Hawk algorithm (named ERTH) based on multiple elite policies and chaotic mapping, while applying this approach in conjunction with the proposed scheduling model to optimize the efficiency of task scheduling in cloud computing environments. We apply the ERTH algorithm to a real cloud computing environment and conduct a comparison with the original RTH and other conventional algorithms. The proposed ERTH algorithm has better convergence speed and stability in most cases of small and large-scale tasks and performs better in minimizing the task completion time and system load cost. Specifically, our experiments show that the ERTH algorithm reduces the total system cost by 34.8% and 36.4% relative to the traditional algorithm for tasks of different sizes. Further, evaluations in the IEEE Congress on Evolutionary Computation (CEC) benchmark test sets show that the ERTH algorithm outperforms the traditional or emerging algorithms in several performance metrics such as mean, standard deviation, etc. The proposal and validation of the ERTH algorithm are of great significance in promoting the application of intelligent optimization algorithms in cloud computing.

A stochastic optimization procedure to design the fair aggregation of energy users in a Renewable Energy Community

Optimizing unit sizes and operation within a Renewable Energy Community (REC) can match intermittent renewable energy generation with variable user energy demands. These uncertain variables are often represented by pre-defined stochastic scenarios, without searching for the “best” scenarios and testing the optimization models with these scenarios. Moreover, little work both optimized RECs under uncertainty and distributed optimal life-cycle costs (investment and operation) among members. Thus, the objectives are: i) identifying the “best” set of stochastic scenarios of solar irradiance and user electricity demands and ii) assessing the accuracy of the “stochastic forecasts” of the total system costs and unit sizes, obtained by solving a stochastic programming model based on the “best” scenarios. The proposed novel procedure shifts the “present moment” back in time to split historical data into “past” and “future” periods used to identify the “best” scenarios and compare the “stochastic forecasts” with the utopic “perfect forecasts” based on the perfect knowledge of real data, respectively. The small errors between these forecasts in the optimal life-cycle costs (less than 2 %) and sizes (3–13 %) indicate good effectiveness of the suggested procedure. Also, the optimal life-cycle costs of “stochastic forecasts” are fairly distributed among users by applying the Shapley value mechanism.

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

A distributionally robust optimization approach of multi-park integrated energy systems considering shared energy storage and Uncertainty of Demand Response

To enhance the economic efficiency and renewable energy integration capacity of multi-park integrated energy systems (MPIES) and address the issue of insufficient consideration of demand response uncertainty in existing studies, this paper proposes a distributionally robust optimization approach for multi-park integrated energy systems, considering shared energy storage and the uncertainty of demand response. First, models of the shared energy system and demand response are established. Based on these models, a deterministic optimization scheduling model for MPIES is developed, aiming to minimize system costs while considering constraints such as grid power balance. To address uncertainty in demand response, this paper employs Interval Type-2 Fuzzy Theory to construct uncertainty sets and considers system reliability constraints. The original optimization problem is then transformed into an equivalent robust counterpart model, and the optimal distributionally robust solution is obtained through parameter domain decomposition methods. Finally, the proposed method is validated using the IEEE 33-bus system. The results show that considering shared energy storage and demand response individually can reduce total system costs by 4.86 % and 26.46 %, respectively. After accounting for the uncertainty in demand response, the total system cost increases only slightly by 4.36 %, but this improves the system's robustness.

Multi-objective energy management using a smart charging technique of a microgrid with the charging impact of plug-in hybrid electric vehicles

The Microgrid (MG) concept is being developed to better integrate renewable energy sources and automate distribution networks. Microgrids combine distributed generating units (DGs) and energy storage systems to achieve this. This research paper aims to simultaneously minimize the daily operational cost and net environmental pollution of a small MG system, factoring in the charging demand from Plug-in-Hybrid Electric Vehicles (PHEVs) and consumer load demands. The proposed energy management process not only minimizes operational costs and emissions, but also determines the optimal battery size for the energy storage system. The analysis also explores the importance of two critical variables - the operation and maintenance costs of the DGs, and the total daily cost of the battery energy storage system. The demand for PHEV charging is managed using an intelligent charging approach. Given the complexity of the optimization, a recently developed metaheuristic algorithm, Slime Mould Algorithm (SMA), is applied. The performance of SMA is compared against the Grasshopper Optimization Algorithm and Sine Cosine Algorithm. To solve the multi-objective problem, a weighted sum method maintaining non-dominance and a fuzzy decision-maker technique are employed alongside the suggested algorithms. Three different scenarios verify the proposed method's effectiveness.

Power system benefits of simultaneous domestic transport and heating demand flexibility in Great Britain’s energy transition

The increasing share of variable renewables in power generation leads to a shortage of affordable and carbon neutral options for grid balancing. This research assesses the potential of demand flexibility in Great Britain to fill this gap using a novel linear optimisation model PyPSA-FES, designed to simulate optimistic and pessimistic transition pathways in National Grid ESO Future Energy Scenarios. PyPSA-FES models the future power system in Great Britain at high spatiotemporal resolution and integrates demand flexibility from both smart charging electric vehicles and thermal storage-coupled heat pumps. The model then optimises the trade-off between reinforcing the grid to align charging and heating profiles with renewable generation versus expanding dispatchable generation capacity. The results show that from 2030, under optimistic transition assumptions, domestic demand flexibility can enable an additional 20–30 TWh of renewable generation annually and reduce dispatchable generation and distribution network capacity by approximately 20 GW each, resulting in a total cost reduction of around £5bn yearly. However, our experiments suggest that half of the total system cost reduction is already achieved by only 25% of electric vehicles alone. Further, the findings indicate that once smart electric vehicle charging reaches this 25% penetration rate in households, minimal benefits are observed for implementing smart 12-hour thermal storages for heating flexibility at the national level. Additionally, smart heating benefits decrease by 90% across all metrics when only pre-heating (without thermal storages) is considered. Spatially, demand flexibility is often considered to alleviate the need for north–south transmission grid expansion. While neither confirmed nor opposed here, the results show a more nuanced dynamic where generation capacities are moved closer to demand centres, enhancing connectivity within UK sub-regions through around 1000 GWkm of additional transmission capacity.

Single-vendor single-buyer multi-product economic production quantity problem with stochastic constraints: a modified generalized elimination method

This study designs a single-buyer single-vendor multi-product Economic Production Quantity (EPQ) model to optimize the total cost in an inventory system. To do so, a Non-Linear Programming (NLP) model is developed considering a set of stochastic constraints on space or warehouse capacity, backordering cost, procurement, ordering, and obtainable budget. A modified Generalized Elimination Method (GEM) is then offered to treat the complexity of the model wherein the variables are eliminated by adding two equations. The developed method generates much more high-quality solutions compared with the classic GEM, while the number of iterations slightly increases. Three problem instances in different scales are then taken into account to assess the efficiency of the modified GEM in terms of optimality criteria. The results demonstrate that the modified GEM has an excellent performance with respect to optimal solutions, infeasibility, number of iterations, complementarity, and errors of optimality. Finally, the behavior of the objective function is analyzed against order quantity fluctuations to draw out practical implications.

Fuzzy credibility chance-constrained multi-objective optimization for multiple transactions of electricity–gas–carbon under uncertainty

This paper proposes a fuzzy credibility chance-constrained multi-objective optimization model to optimize market transactions in the electricity–gas–carbon sectors under uncertainty. The model aims to maximize the profit of an integrated energy service provider by incorporating a ladder-type carbon trading mechanism, which adjusts carbon prices based on emission levels, and detailed multi-energy flow constraints. To effectively manage uncertainties in electricity, gas, and carbon markets, we derive credibility distributions for uncertain variables and introduce fuzzy credibility chance constraints—tools that assess the likelihood of meeting multiple transactions under uncertainty. The proposed model hedges the risks associated with multiple uncertainties while maximizing the credibility of expected costs, effectively balancing risk and cost. Through simulation analysis on an IEEE 33-node power network and a 32-node heat network, the proposed model achieved a 9.8% reduction in total system cost and an 8.5% reduction in total carbon emissions. Additionally, the model effectively determined credibility levels under different risk preferences, demonstrating its robustness in enhancing electricity–gas–carbon trading and promoting a low-carbon economy. This research provides a novel planning method for formulating trading strategies in multi-energy markets, with significant real-world implications for energy management and environmental sustainability.

Joint optimization of production, inspection, and maintenance under finite time for smart manufacturing systems

Given the flexible and configurable characteristics of smart manufacturing systems with a limited time per manufacturing task, the assumption of infinite time for prostems is no longer applicable to the joint-control strategy. Consequently, a joint-control model that considers production, inspection, and maintenance within a finite-time scenario for smart manufacturing systems is proposed in this paper. The objective is to optimize overall production and maintenance functions to minimize the total system cost. Comparing the joint strategy under infinite time with the proposed finite-time approach reveals significant differences in unit costs between the two scenarios. To enhance the effectiveness of the model, a discrete iterative algorithm with multiple loops was developed. Through a case study, it was observed that 1) joint strategies implemented within a finite time horizon were more cost-effective than those under infinite time, thus emphasizing the need for business managers to develop strategies within a finite time frame; 2) different production planning and efficiency levels had varying effects on the final joint strategy, necessitating customized strategies based on different production durations. Overall, the research gap regarding joint strategies within a finite-time context was addressed in this research, serving as a methodological foundation for practitioners to develop various strategies that minimize total costs across diverse real-world scenarios.

Optimised Two-Layer Configuration of SESS-CCHP System Considering Wind and Light Output Correlation and Load Sensitivity

With the gradual depletion of fossil energy sources and the diversification of users’ energy demand, combined cooling, heating and power (CCHP) microgrids have become a hot technology to improve energy efficiency and promote efficient and synergistic energy operation. However, the uncertainty and correlation of wind power and photovoltaic (PV) outputs have posed a great challenge to the reliability of CCHP system operation, so CCHP systems are often equipped with energy storage devices to improve system flexibility to ensure the reliability of energy supply. However, system-owned reserves still have shortcomings such as high investment O&M costs and large space requirements. As an emerging model, “shared energy storage” can reduce the investment pressure of users and open up new ways for the economic and stable operation of CCHP systems. Therefore, based on the scenario of wind and solar power correlation and considering different types of load flexibility, this paper proposes to construct a shared energy storage station (SESS)-CCHP double-layer synergistic optimal allocation model. The model incorporates the consideration of the actual operation strategy of the CCHP system in the planning stage of energy storage. An example analysis shows that SESS reduces the total operating cost of the CCHP system by 25.96% and improves the new energy consumption rate by 10.46% compared with no energy storage. Compared with the system independently configured with energy storage, the cost saving is 2.14%, thus validating the effectiveness of the proposed model.

Integration of smart charging of large-scale electric vehicles into generation and storage expansion planning: a case study in south china

This paper studies how to integrate the smart charging of large-scale electric vehicles (EVs) into the generation and storage expansion planning (GSEP), while analyzing the impact of smart charging on the GSEP of a real power system in south China. For this purpose, a random simulation-based method is first developed to provide the tractable formulations of the adjustable charging load and reserve provision from EVs. This method avoids the unrealistic assumption that EVs drive and charge every day, which often exists in prior relevant approaches. Based on the random simulation, this paper proposes a novel GSEP optimization model which incorporates the weekly adjustable charging load of EVs. In the proposed model, the total charging load of EVs can be co-optimized with the investment and operational decisions of various generation and storage units. This GSEP model is applied to a provincial power system in south China. The numerical results show that the implementation of smart charging can significantly alter the decisions of GSEP. As the participation rate of smart charging improves from 0% to 90%, there is an additional 1,800 MW installation in wind and solar power, while the need to build new batteries is noticeably reduced; also, depending on the level of EV uptake, the annualized total system cost decreases by 5.11%–7.57%, and the curtailment of wind and solar power is reduced by 10.34%–19.64%. Besides, numerical tests reveal that the traditional assumption that EVs drive and charge every day can mislead the evaluation of adjustable charging load and overestimate the daily charging power peak by averagely 24.72%.

Combined effect of climate change and labor relation on optimally managing labor productivity under uncertainty

Heat stress amplified by climate change causes excessive reductions in labor capacity, work injuries, and socio-economic losses. Yet studies of corresponding impact assessments and adaptation developments are insufficient and incapable of effectively dealing with uncertain information. This gap is caused by the inability to resolve complex channels involving climate change, labor relations, and labor productivity. In this paper, an optimization-based productivity restoration modeling framework is developed to bridge the gap and support decision-makers in making informed adaptation plans. The framework integrates a multiple-climate-model ensemble, an empirical relationship between heat stress and labor capacity, and an inexact system costs model to investigate underlying uncertainties associated with climate and management systems. Optimal and reliable decision alternatives can be obtained by communicating uncertain information into the optimization processes and resolving multiple channels. Results show that the increased heat stress will lead to a potential reduction in labor productivity in China. By solving the objective function of the framework, total system costs to restore the reduction are estimated to be up to 248,700 million dollars under a Representative Concentration Pathway of 2.6 (RCP2.6) and 697,073 million dollars under RCP8.5 for standard employment, while less costs found for non-standard employment. However, non-standard employment tends to restore productivity reduction with the minimum system cost by implementing active measures rather than passive measures due to the low labor costs resulting from ambiguities among employment statuses. The situation could result in more heat-related work injuries because employers in non-standard employment can avoid the obligation of providing a safe working environment. Urgent actions are needed to uphold labor productivity with climate change, especially to ensure that employers from non-standard employment fulfill their statutory obligations.

Techno-economic analysis of the integration of an innovative particle-based concentrating solar power system with a thermally driven cooling system

Stand-alone solar cooling technologies are under development and cannot compete economically with conventional cooling systems. Integration of particle-based concentrating solar power (PBCSP) systems with thermally driven cooling systems can provide an advantage over stand-alone solar cooling systems by providing low-cost, eco-friendly electricity and cooling energy. Consequently, this research proposes to investigate and identify the best configuration for the integrated system deployment to provide electricity and cooling energy in Tabuk province in Saudi Arabia and evaluate the levelized cost of electricity (LCOE) for the particle-based concentrating solar power and the levelized cost of cooling (LCOC) for the thermally driven cooling systems. Tower height and receiver dimensions of the particle-based concentrating solar power are found by performing techno-economic optimization in SolarPILOTTM and SAMTM. The performance of the particle-based concentrating solar power block and the thermally driven cooling systems is evaluated by simulating the thermodynamic model in EESTM. These models are validated by the manufacturers’ datasheets. Cost models are defined to be used in the economic analysis. The exhaust gas double-effect absorption chiller (EGDEAC) is selected as the thermally driven cooling system. The result of thermodynamic performance analysis shows the particle-based concentrating solar power has an annual electricity production of 191 TWh from solar energy alone, with the power block exhaust having an average flow rate of 386 Mg/h and an average yearly exhaust temperature of 378◦C. On the other hand, the exhaust gas double-effect absorption chiller produces an annual cooling energy of 97,461,948 TR-h with an average COP of 1.456. The economic results demonstrate that the proposed system achieves a levelized cost of electricity, and levelized cost of cooling of 6.08 ¢/kWh and 3.77 ¢/TR-h, respectively. When this levelized cost of cooling is compared with the conventional mechanical vapor compression (MVC) cooling system, the result shows that the exhaust gas double-effect absorption chiller is competitive and has one-third of the levelized cost of cooling of mechanical vapor compression. The sensitivity analysis was also made on related influencing factors for the levelized cost of cooling. The analysis shows that the levelized cost of cooling of exhaust gas double-effect absorption chiller is sensitive to the amount of the output cooling energy and the total system cost.

Optimal design of concentrated solar power-based hydrogen refueling station: Mixed integer linear programming approach

This paper developed a mixed integer linear programming model to optimally design hydrogen refueling station coupled with an on-grid concentrated solar power. The model aims to minimize the total life cycle cost of the integrated system addressing various constraints. The utility of the proposed model is validated using hydrogen refueling station under the weather conditions of Dhahran city, Saudi Arabia. The station fulfills the daily hydrogen requirements of taxi-fleet in Dhahran city, estimating a daily demand of 4202 kg of hydrogen. The proposed hydrogen refueling station requires a solar field of 71,721 square meters to generate 50,233 MWh of energy annually. The on-grid concentrated solar power system is determined to produce an excess energy of 10,515 MWh which is exported to the grid, highlighting positive economic impact. The levelized cost of hydrogen of the proposed system is 7.17 $/kg. The achieved results validated the feasibility and efficiency of the proposed system, positioning it as a sustainable solution for meeting the rising demand for clean and green transportation in the region.

Optimal operation of co‐phase traction power supply system with HESS and PV

AbstractThe co‐phase traction power supply system (TPSS) with hybrid energy storage system (HESS) and photovoltaic (PV) is proposed to eliminate the neutral section and improve the regenerative braking energy (RBE) utilization. Although the integration of HESS and PV facilitates the energy saving and cost reduction of the co‐phase TPSS, the high cost and configuration of HESS should be considered, which is the key to affect the optimal operation strategy of co‐phase TPSS. Here, the optimal operation strategy of co‐phase TPSS with HESS and PV is proposed to design the HESS configuration, recycle RBE and improve power quality. The proposed model aims to minimize the total system cost, including HESS investment cost, electricity cost and operation and maintenance cost. Moreover, the proposed model is formulated as a mixed integer linear programming by employing linearization approaches. Finally, case studies verify that the 29.2% cost reduction rate is achieved and the three‐phase voltage unbalance meets the standard requirements.

Distributed Cooperative Dispatch Method of Distribution Network with District Heat Network and Battery Energy Storage System Considering Flexible Regulation Capability

Flexible resources, including district heat networks (DHN) and battery energy storage systems (BESS), can provide flexible regulation capability for distribution networks (DN), thereby increasing the absorption capacity for renewable energy. In order to improve the operation economy of DN and ensure the information privacy of different operators, a distributed cooperative dispatch method of DN with DHN and BESS considering flexible regulation capability is proposed. First, a distributed cooperative dispatch framework of DN-DHN-BESS is constructed. Then, an optimal dispatch model of DHN under constant flow-variable temperature control strategy is established in order to utilize the heat storage capacity to provide flexible regulation capability for DN. Next, the optimal dispatch models of BESS and DN are established with the objective of minimizing the operation cost, respectively. Finally, a solution method based on the alternating direction multiplier method of distributed cooperative dispatch for DN-DHN-BESS is proposed. Case studies are performed on a system consisting of a 33-node DN and a 44-node DHN, and simulation results demonstrate that the proposed method differs from the centralized dispatch method by only 0.52% in the total system cost, and the proposed method reduces the total system cost by 34.5% compared to that of the independent dispatch method.

Integrated foodbank network design: Model and a case study

To address the UN’s zero hunger goal (SDG 2), scattered and isolated initiatives by nonprofit organizations towards operating foodbanks are generally ineffective in developing countries where the foodbank ecosystem is at a preliminary stage. Establishing an integrated system comprising entities such as donors, foodbanks, food recovery and redistribution agencies (FRRA), and beneficiaries can be quite complex due to an underlying hierarchy, scale of operation, types of donors, and the severity of food insecurity of the beneficiaries. In this work, we present a strategic mixed-integer programming model to design an integrated foodbank network towards achieving an efficient, effective, and equitable food distribution mechanism for food-insecure beneficiaries while accounting for their age profile and nutritional requirements. We ensure cost-efficiency by minimizing the total system cost, effectiveness by discouraging food waste and unmet demand via charging penalties, and equity by adopting five variants of an egalitarian approach. We conduct a case study with a mix of real and realistically estimated data to design a foodbank network in Delhi (India) and present detailed analyses with insights for the practitioners. Specifically, the effects of foodbanks’ initial capacities, budget and strategic-to-operational cost constraints on the solution are identified. Among important observations, our analyses highlight when initiatives for collecting more ready-to-eat foods might be taken to relieve the pressure on the integrated system, and also help in identifying the conditions when investment in capacity building serves the beneficiaries’ interests better than direct spending.