Total Operating Cost Research Articles

Industrial multi-energy communities as grid-connected microgrids: Understanding the role of asymmetric grid-charge regulation

The industrial sector is currently the leading emitter of greenhouse gases worldwide. Lowering emissions, the collaborative use of energy and storage technologies in Industrial Energy Communities (IEC) is a promising option, typically implemented as a grid-connected microgrid. To support successful implementations of IECs, it is essential to understand not only the interaction of different technical assets within an IEC but also the corresponding regulation that determines the IEC's economic and ecological performance. Similar to different technical capabilities of available assets, companies of an IEC are typically affected by regulation in different, asymmetric ways. To the best of our knowledge, we are the first to investigate the economic and ecologic effects stemming from asymmetric regulation, i.e., regulation that differs between different participating companies via a microgrid approach. By developing a novel linear model for German asymmetric grid charge regulation, we are able to optimize the economic operation of complex multi-energy microgrids under detailed regulatory conditions. In more detail, we formulate and implement a mixed-integer linear program to investigate the joint operation of a multi-energy IEC under asymmetric regulation. We conduct a real-world case study to evaluate the effects of German grid-charge regulation as a significant example of asymmetric regulation and compare the results of our IEC to a situation where every company of the IEC manages its assets individually. Our results indicate that IECs have the potential to significantly reduce the total operational energy costs under the current asymmetric German grid-charge regulation. While the shared assets see a higher utilization in the IEC, the impact on emissions is, however, limited.

Dynamic fleet management: Integrating predictive and preventive maintenance with operation workload balance to minimise cost

The optimization of fleet maintenance management is of utmost importance to ensure the efficient and reliable operation of asset fleets. Traditional maintenance strategies are often reactive or rely on predetermined schedules, which can lead to inefficient resource allocation and increased operational costs. The advent of digital technologies has allowed the integration of predictive maintenance into fleet management. This paradigm shift towards a more data-driven approach enables fleet management to dynamically respond to issues identified through sensors and algorithms that detect anomalies and provide prognostic insights regarding the remaining useful life of components. However, a notable deficiency exists in the integration of predictive maintenance with calendar-based preventive maintenance and fleet operational allocation, thereby impeding value creation for businesses. This paper presents an optimisation model to address this issue by incorporating preventive and predictive maintenance while simultaneously striving to balance the workload to meet operational demand and mitigate potential penalties resulting from failure to meet these demands. The paper also examines the role of component criticality as well as the precision of the RUL (Remaining Useful Life) prognosis. Through the experiments conducted, it has been demonstrated that the allocation of the fleet is subject to change depending on the level of criticality of monitored components. These findings reveal the potential risks and penalties that could arise from an insufficient definition of failure impact severity when integrating predictive maintenance with existing preventive approaches and operational workload balance. Additionally, the experiments underscore the impact of RUL distributions precision on total operating costs, showing the confidence intervals through a sensitivity analysis.

Optimizing routing and scheduling of shared autonomous electric taxis considering capacity constrained parking facilities

This paper focuses on routing and scheduling of autonomous electric vehicles to provide reservation-based shared ride services, while a set of parking facilities with limited capacity are used for vehicle intermittent charging. A mixed-integer linear program model is formulated in the form of a vehicle routing problem with satellite facilities (VRPSF), subject to a series of additional time and capacity-related constraints. The objective of the model is to minimize the total operating costs of the system, including those related to vehicle miles traveled and the deployed vehicle fleet size. The number of vehicles inside each parking facility is tracked so as to ensure that the capacity is never exceeded throughout the service horizon. A customized solution method based on an adaptive large neighborhood search algorithm with an explicit treatment of parking facility choices is developed. A series of numerical experiments, consisting of both hypothetical examples and a real-world case study in Hangzhou, China, have been conducted to evaluate the effectiveness and applicability of the proposed model and algorithm. The results demonstrate that ride-sharing services and parking facilities have the potential to significantly reduce the total vehicle energy consumption and operating costs for a shared autonomous electric taxi (SAET) operator in practical scenarios.

Reducing blood product wastage through the inter-hospital redistribution of near-outdate inventory.

Hospital transfusion services order blood products to satisfy orders and maintain inventory levels during unexpected periods of increased blood demand. Surplus inventory may outdate before being allocated to a recipient. Blood product outdating is the largest contributor to blood wastage. A province-wide redistribution program was designed and implemented to redistribute near-outdate plasma protein and related blood products from low-usage to high-usage hospitals. Program operations and details are described in this paper. Two transport container configurations were designed and validated for transport of all blood products. A cost-analysis was performed to determine the effectiveness of this redistribution program. A total of 130 hospital transfusion services contributed at least one near-outdate blood product for redistribution between January 2012 and March 2020. These services redistributed 15,499 products through 3412 shipments, preventing the outdating of $17,570,700 CAD worth of product. Program costs were $14,900 for shipping and $30,000 for staffing. Failed time limits or non-compliance with packing configurations resulted in $388,200 worth of blood products (97 shipments containing 816 products) being discarded. Courier transport delays was the most common reason (42/97; 43%) for transport failure. Redistributing near-outdate blood products between hospitals is a feasible solution to minimize outdating. Despite heterogeneity of Canadian blood product inventory, all products (each with unique storage and transport requirements) were successfully redistributed in one of two validated and simple containers. Total operation costs of this program were small in comparison to the $17.6 million in savings associated with preventing the discard of outdated products.

Online Integrated Production and Distribution Scheduling: Review and Extensions

As a growing number of manufacturers adopt the make-to-order business mode and a growing number of retailers sell online, we are seeing numerous decision problems that can be modeled as what are known in the literature as integrated production and distribution scheduling (IPDS) problems. In such problems, order processing and delivery must be scheduled jointly in order to achieve an optimal balance between total operational costs and overall customer service. Offline IPDS problems, in which the information about every order is known in advance with certainty, are extensively studied. However, research on online IPDS problems, in which orders arrive randomly with their information unknown until they arrive, is relatively recent but is growing rapidly. In this paper, we first describe several real-world applications to illustrate the importance of studying online IPDS problems from a practical point of view. We then review the existing literature on online IPDS problems with a focus on existing online algorithms for these problems and their theoretical performance. We also derive some new results to fill several gaps left in the literature and discuss possible topics for future research. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0305 .

Enhancing in situ remediation of clayey soils contaminated with total petroleum hydrocarbons by combining pneumatic fracturing, plasma blasting, and vacuum extraction: A comprehensive field investigation

Oxidant injection technology is widely used in soil remediation; however, subsurface conditions, including soil properties and permeability, significantly influence its effectiveness. In this study, the potential of the combination of pneumatic fracturing, plasma discharge, and vacuum extraction (PPV) in enhancing soil permeability and remediating field-scale silt/clayey soils contaminated by total petroleum hydrocarbon (TPH) was studied. Improved soil permeability was assessed by measuring the chlorine and H2O2 concentrations in soil samples, and the remediation efficiency of PPV was evaluated by analyzing the TPH concentrations in the soil samples. The initial highest TPH concentration was 6922 mg kg−1; after a 42-day operation period, the TPH levels decreased to 541 mg kg−1. The economic assessment of PPV revealed it to be cost-effective, with a total operating cost of US$ 3077 for remediating 265 m³ of contaminated soil, equating to US$ 11.6 per ton of soil. Since PPV requires a short operational period, is inexpensive, and is suitable for purifying low-permeability soil, it is a promising and economically advantageous approach for soil purification. Overall, PPV demonstrated the ability to enhance oxidizing agent transfer by increasing soil permeability, suggesting its potential for efficient and targeted soil remediation.

Clean energy pipeline energy storage system and its economy

The economic problem of a clean energy heating system under a peak and valley electricity pricing system is investigated, and a pipe network energy storage system is correspondingly proposed to solve this problem. The system makes use of large-capacity primary network pipe network water storage to store heat during the valley electricity hours when the electricity price is lower, and releases the stored heat to supply heat during the peak electricity hours when the electricity price is higher, with a view to reducing the operating time of the heating system during the peak electricity hours when the electricity price is higher. The principle of the system and the efficient regulation scheme are given, and a mathematical model of primary pipe network energy storage and release is established, and the reliability of the model is verified with a heating project in Qingdao as an example, and the results show that the maximum temperature deviation of the return temperature of the primary pipe network obtained by the calculation of the energy storage and release model is 0.23 °C, and the maximum temperature hysteresis error is in 15 %, which meets the requirements of engineering practice. Based on the energy storage model, the simulation of the actual heating economy of the project using the pipe network energy storage system was carried out, and the results showed that compared with the original scheme based on the maximum heat load adjustment of the proportion of heating units and boilers, the electricity consumption of the project using the pipe network heat storage scheme was increased by 18.8 % in the whole heating season, the total operating costs were reduced by 14.5 %, with an annual saving of 800,600 yuan in operating costs, and the payback period for the investment was relatively short, at 2.1 years, which is of good economy.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

Optimal capacity configuration and dynamic pricing strategy of a shared hybrid hydrogen energy storage system for integrated energy system alliance: A bi-level programming approach

The shared energy storage system is recognized as a promising business model for the coordinated operation of integrated energy systems (IES) to improve the utilization of energy storage and the consumption of renewable energy. As the hydrogen energy gradually receives more attention, this paper constructs the structure of a hybrid hydrogen energy storage system shared by an IES alliance in a dynamic pricing mode. A bi-level optimization model for the shared hybrid hydrogen energy storage system (SHHESS) is proposed to optimize the capacity configuration decisions and the pricing strategy jointly. The upper level determines the capacity and dynamic price of SHHESS with maximum profits and the lower level obtains the optimal operation of the IES alliance minimizing the total operation costs. The proposed model is solved by an improved PSO-GA algorithm and CPLEX solver. Finally, numerical tests are conducted in different scenarios. The results show that the hybrid energy storage system improves the daily profits of SHHESS by 70.3% and 5.44%, and reduces the renewable energy curtailment by 80.93% and 48.92% respectively compared to the battery-only and hydrogen-only systems. Additionally, the daily operation cost of the IES alliance is 35.2% lower than the individual operation scenario. The proposed model confirms the feasibility and superiority of the hybrid hydrogen energy storage system in a sharing business mode.

A short-term wind-hydrothermal operational framework in the presence of pumped-hydro storage

Short-term operation is one of the most significant aspects of power systems operation and planning and it mainly includes resource scheduling including renewable and non-renewable energy sources. The mentioned problem is modeled as an optimization problem with one or more objectives depending upon the priorities of the decision maker (system operator) and several equality and inequality constraints. In this respect, this paper proposes a short-term operational model for the hydrothermal power systems in the presence of wind power generation and pumped-hydro storage (PHS) units. The problem is formulated as a single-objective optimization problem that aims to minimize the total operating cost including the fuel cost of thermal units. The problem has been comprehensively investigated through five case studies where the first case study is used for the sake of validation. Then, the other case studies assessed the impacts of wind power generation, PHS unit, and uncertainty on the problem. The five case studies are as follows: Case 1: Short-term hydrothermal (STH) scheduling which aims to minimize the total cost. Case 2: Deterministic short-term hydrothermal-wind (STHTW) scheduling which aims to minimize the total cost. Case 3: Stochastic wind-hydro-thermal scheduling that seeks to minimize the total cost taking into consideration the uncertainty of the wind power generation. Case 4: Deterministic wind-hydro-thermal scheduling in the presence of the PHS unit with the total cost minimization as the objective function. Case 5: Stochastic wind-hydro-thermal scheduling in the presence of the PHS unit that is used to evaluate the impact of uncertainty of wind power generation with the total cost minimization as the objective function. It is noteworthy that the first three case studies have been formulated using a non-linear programming (NLP) model and solved using the CONOPT solver in GAMS. Case studies 4 and 5 have been presented using a mixed-integer non-linear programming (MINLP) model and solved using the DICOPT solver in GAMS. A sensitivity analysis has also been carried out to assess the system's performance with different loading conditions. In this respect, the total cost and PHS operation with different amounts of load demand were evaluated and the results have been presented and discussed.

Technical and economic impact of water reuse as an integrated water resource management measure in rural water supply systems

ABSTRACT Water reuse becomes an alternative to reduce the demand in the conventional water supply systems, especially in regions, where non-potable use of drinking water, as for garden and grass irrigation, is predominant. This study evaluates the footprint of reuse on small water supply systems in rural areas, where raw water quality does not meet the drinking water standards and complicated treatment is needed. Individual facilities for reuse of potential rainwater, light gray, gray and domestic wastewater in the households could lead to a decrease in non-potable tap water use up to 60% and a decrease of households' annual expenses for water supply up to 93 €/household. The installation of individual facilities for common reuse of rainwater and gray wastewater requires the highest investment costs, but the option`s operational costs are between 15% and 20% lower than all other options considered. The Drinking Water Treatment Plant capacity reduction due to Integrated Water Resource Management measures implementation enables from 48% up to 58% saving in the total operational costs for drinking water supply in the settlement. The shortest payback period and best economic impact of reuse for small scale water supply systems is observed if rainwater and gray wastewater are reused together.

Battery swapping, vehicle rebalancing, and staff routing for electric scooter sharing systems

Electric scooter (e-scooter) sharing systems provide on-demand electric scooter rental services. E-scooters are equipped with swappable batteries managed by staff who visit each scooter and replace depleted batteries with charged ones. The e-scooters in this system are free-floating; they can be located anywhere without having to be returned to designated stations. Due to this characteristic of the scattered location of e-scooters, operation decisions with associated staff routing are more challenging than station-based services. Thus, it is critical to implement the efficient management of e-scooter redistribution and charging decisions with proper staff routing to successfully prepare for user demand within a limited operation time while minimizing the total operating cost. To this end, we introduce a battery swapping and vehicle rebalancing problem with staff routing for e-scooter sharing systems, formulated as a mixed integer programming (MIP) model. To derive solutions efficiently for a large-scale instance in practice, we propose a clustered iterative construction approach where the problem is decomposed into two phases. The first phase clusters regions using an approximation of intra-region and inter-region operation costs with the minimum spanning tree approach. The second phase efficiently derives multiple candidate regional sequences by our partial permutation procedure and following the sequences, iteratively solves a significantly reduced size of the problem to construct operation assignments. Our numerical experiments on the generated instances and real-world instances demonstrate that the proposed two-phase algorithm shows significantly superior performance in practically large scale instances than the benchmarks without clustering or the iterative procedures of our algorithm.

Route, speed, and bunkering optimization for LNG-fueled tramp ship with alternative bunkering ports

To reduce emissions, shipping companies are deploying eco-friendly ships, like those powered by LNG or methanol. Unlike traditional fuels, LNG bunkering is available at a limited number of ports. At the same time, tramp or bulk ships have fewer ports of call on their shipping lines, which may necessitate LNG-fueled ships sailing to alternative bunkering ports for refueling. The selection of bunkering ports impacts the operating costs of shipping lines due to differences in fuel prices and geographical locations. Moreover, it must factor in ship speed limits to meet loading and unloading schedules. To optimize the shipping route, speeds, and bunkering plan for LNG-fueled tramp ships, we develop an arc-based mixed-integer linear programming model with the objective of minimizing total operation costs. Subsequently, we propose a leg-based formulation based on the predetermined port call orders. Finally, case studies are conducted to demonstrate the effectiveness and efficiency of the models. Experimental results indicate that the bunkering mode with alternative bunkering ports effectively reduces total operating costs without altering the total voyage time, particularly with substantial differences in fuel prices between ports.

A Two‐Stage Multi‐Objective Optimal Scheduling Model for Community Integrated Energy System

Community integrated energy system coupled with renewable energy generation provides an effective solution to improve economy and reduce carbon emissions. This paper establishes a two‐stage multi‐objective optimal scheduling model for community integrated energy system. During the day‐ahead scheduling stage, a comprehensive customer dissatisfaction model based on Kano model is established, and the model takes total operating cost, comprehensive customer dissatisfaction, and carbon emissions as multi‐objectives. The Non‐dominate Sorting Genetic Algorithmic‐II (NSGA‐II) and the CRITIC‐TOPSIS evaluation model are used to develop a day‐ahead scheduling scheme. On the premise of ensuring customer dissatisfaction, the intra‐day scheduling stage evens out the uncertainty of wind power and photovoltaic power through rolling optimization and improves the reliability of system operation. Simulation results show that the proposed model reduces the total operating cost and carbon emissions by 8.1% and 12.8%, respectively, which validates the effectiveness of the proposed model. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Joint planning for PV-SESS-MESS in distribution network towards 100% self-consumption of PV via consensus-based ADMM

The integration of distributed photovoltaic (PV) has become a prominent characteristic of distribution network (DN). However, achieving 100% PV self-consumption poses challenges due to intermittent generation and varying load demands. To address these challenges, this paper proposes an innovative joint planning method for PV, stationary energy storage system (SESS), and mobile energy storage system (MESS) using the consensus-based alternating directional multiplier method (ADMM). First, to account for multiple scenarios with real-world variations, a two-stage clustering method with clustering by fast search and finding of density peaks and k-means is employed. Second, a joint planning model for PV, SESS, and MESS is developed to minimize the total cost of DN, including installation, operation, and maintenance costs. The model optimizes the PV capacity, SESS capacity, and MESS capacity, as well as their locations. Third, a distributed planning framework is proposed using consensus-based ADMM, which decomposes the problem into a PV planning subproblem under the worst-case scenario and energy storage system planning subproblems under multiple scenarios. Finally, the proposed method is demonstrated on an IEEE-33 node system, and the results show the method effectively enhances the access capacity of PV by 11.80% and leads to a reduction in the total cost.

New methodology for estimating the influence of Cu content of Ferrous Scrap materials in the Total Cost of Operation (TCO) of scrap mix in Electric Arc Furnace steelmaking

Scrap is the main raw material of the electric arc furnace (EAF) route of steelmaking and an important one in the primary route. The decarbonisation commitment acquired by the most relevant steelmaking companies, will only increase the amount of scrap used by both routes, making it a critical component to succeed in the worldwide challenge of reducing CO2 emissions. While being a very relevant material, scrap characterisation is complex due to its heterogeneity, but furthermore, its quality is worsening as its demand increases. This includes the sterile content, such as copper. Higher sterile content scrap comes at a lower price, while scrap with a lower content in undesired elements is most costly. Being able to balance the cost and benefit of purchasing scrap with a higher or lower content of copper with a respective, lower or higher purchase cost would be of interest to optimise the mix and produce the required quality steel at the lowest possible cost. Nowadays, this cost benefit analysis can only be done by performing Total Cost of Operation (TCO) calculations that require a considerable amount of information, including but not limited to the complete chemical composition of scrap, market parameters, production forecast and other relevant data; moreover, involving an arduous optimization process. Based on a parametric study of the TCO calculation, this work proposes a mathematical model to estimate the impact on the TCO of a predefined scrap mix when only modifying the specifics of one given scrap type. The results would facilitate the decision making by comparing the two scenarios as a factor of cost. This mathematical model can be easily programmed and requires only 5 parameters to be run, while providing a low error result.

Regression based stochastic energy management strategy for a grid connected microgrid using Artificial Electric Field Algorithm

Recent decade has witnessed steep technological advancement in the renewable energy sector due to growing concern of climate change and emission cut target. The lack of availability of fossil fuels and the high price of the same have made the use of Renewable Energy Sources (RES) as a promising approach for generating clean and sustainable energy at local level. This persuades the implementation of Microgrid (MG) supported with RES, to cater various types of load demand. However, planning and optimizing a MG operation have become challenging due to the intermittent nature of RES and uncertain loads. To address this issue, this paper introduces a Stochastic Energy Management Strategy (SEMS) for a grid connected MG comprising Photovoltaic, Wind Turbine, Fuel Cell, Micro Turbine, Battery Energy Storage and electrical as well as heat energy demand. The volatilities of RES power output and energy demand are modeled via Gaussian Process Regression (GPR) and Monte Carlo Simulation (MCS) respectively. The problem is framed as non-linear (NL) dynamic stochastic optimization problem and solved using ‘Artificial Electric Field Algorithm (AEFA)’. Here, minimization of expected value of operational and emission cost are taken as objective functions; while various practicality aspects are modeled as constraints in the problem. Further, the efficacy of the proposed approach is validated around the first central moment of the load. Various case studies are performed to estimate the day ahead optimum operating schedule of the Distributed Energy Resources (DER) and to assess the impact of load uncertainties on DER operation and total MG operation cost. Further, the efficacy of the GPR is verified through comparison with MCS based result.

Optimal scheduling of multi-regional energy system considering demand response union and shared energy storage

In the current context of the scarcity of fossil energy and the large-scale development and utilization of new energy sources, the power system is developing in the direction of multi-source synergy and interconnection. Demand response technology and energy storage technology have become important adjustment means of integrated energy system because of their efficient coordination ability and flexible adjustment ability. However, in the system where multiple energy systems operate cooperatively, there are still some limitations in time and capacity scales. Therefore, in order to enhance the demand-side response capability in multi-energy systems and give full play to the function of energy storage power stations, this paper proposes an optimal scheduling model for multi-area energy systems that considers joint demand response and shared energy storage. First, the system energy coupling matrix is constructed based on energy hubs, and the modeling framework for joint demand-side response and shared energy storage is established. Second, the multi-objective optimization problem is solved using NSGA-II algorithm solution with the optimization objectives of minimizing the total operating cost of the system and maximizing the net environmental impact. Finally, the simulation analysis is carried out. The simulation results show that the addition of joint demand response and shared energy storage can guide the scheduling optimization of multiple energy sources in each region in time and space, and realize the energy complementarity and mutual assistance of multi-regional energy systems. Compared with the traditional multi-regional energy system optimization scheduling scheme, the operation cost of the scheme is reduced by 4.2 %, and the environmental protection is improved by 41.9 %, which proves the feasibility of the optimal scheduling model proposed in this paper.

Research on the participation model of energy storage in electricity spot markets considering energy constraints and cost characteristics

In the context of power systems with a high proportion of renewable energy, energy storage plays a significant role in facilitating the consumption of renewable energy and ensuring the operational safety of power systems. However, the current power spot market's predominant power bidding model does not fully consider the physical and cost-operational characteristics of energy storage, which is not conducive to further incentivizing investment and construction of energy storage, and may indirectly affect the flexibility of energy storage in peak shaving and valley filling. This paper summarizes the key issues that need to be addressed for energy storage to participate in the spot market from two aspects: the power bidding model does not meet the requirements of the physical and cost-operational characteristics of energy storage, and the real-time market under this model cannot achieve optimal allocation of energy storage. Considering the energy constraints and cost characteristics of energy storage, a charge and discharge bidding model is proposed, which is based on the stored energy value of energy storage and is in line with the physical and cost-operational characteristics and real-time optimization needs of energy storage. Subsequently, a market clearing model for energy storage participation in the spot market under the state of energy bidding method is constructed, and based on the IEEE 39-bus test case, a comparative analysis of the nodal electricity prices, energy storage revenue, and total system costs under the proposed market participation model and the traditional power bidding model is conducted. Simulation results show that the proposed energy storage participation model in the spot market can better utilize the value of energy storage in peak shaving and valley filling compared to the conventional power bidding model, reducing the extreme electricity prices by up to 10%, increasing single cycle revenue of energy storage by 46%, and reducing the total operating costs of the system in scenarios with significant deviations in system load in the day-ahead and real-time markets.

Optimizing Integrated Water and Electrical Networks through a Holistic Water–Energy Nexus Approach

As water and electrical networks cannot be entirely independent, a more integrated approach, the water–energy nexus (WEN), is developed. A WEN is the basis of a smart city where water and electrical networks are interconnected and integrated by implementing efficient management strategies. Accordingly, this study develops a dynamic co-optimization model for designing and operating an integrated power and water system. The proposed co-optimization model minimizes the total annual and operational costs of a micro-WEN system while capturing its optimum design values and operating conditions and meeting the demands of the electrical and water networks. Furthermore, this work presents a plan for transitioning from thermal desalination to reverse osmosis (RO) desalination in the United Arab Emirates (UAE). The key objective is to decouple electricity and water production, effectively tackling the issue of operating the UAE’s power plants at low efficiency during the winter while ensuring an adequate water supply to meet the growing demand. The results show that the co-optimization model provides a significant reduction in the total operational cost with the integration of photovoltaic energy and shifting to RO. Most importantly, the micro-WEN system is optimized over multiple timescales to reduce the computation effort and memory requirements.