Minimum Operating Cost Research Articles

Multi-Objective Optimization of Integrated Energy Systems Considering Renewable Energy Uncertainty and Electric Vehicles

In this study, the multi-objective optimization considering renewable energy uncertainty and electric vehicles (EVs) is proposed for regional integrated energy systems (RIES), which can balance the economy and environmental friendliness of the system. Firstly, the influence of factors such as disordered access and charging/discharging of EVs on system operation is considered, an orderly charging and discharging model of EVs is established according to the driving laws. Then, a robust optimization model with polyhedral uncertainty set is constructed to handle the uncertainty of renewable energy output. Moreover, the minimizing operation cost and carbon emissions is developed as the multi-objective function, and the carbon emission penalty factor is introduced to convert the multi-objective into single objective solution. Finally, the validation is carried out by an actual RISE. The case studies verify the proposed method can well balance the economy and environmental protection of the system, and enhance robustness against the uncertainty while realizing the economic and optimal operation of the system.

Cost-effective energy management of an islanded microgrid

The interest in the integration of distributed energy resources in microgrids increased significantly in the last decade. The stochastic nature of some energy sources and the dynamic power demand has brought difficulties regarding the optimal control of microgrids. This current study addresses the energy management challenge in an islanded hybrid energy microgrid that includes three types of renewable energy resources (photovoltaic, geothermal and biomass) and a battery storage system, using intelligent management methods that optimize energy production costs. The proposed intelligent management methods are based on cost function minimization. These ensure optimal energy management operation under uncertain weather conditions, keeping to the minimum the price of the energy produced by the microgrid. Due to the erratic nature of solar irradiance and considering that solar energy is cheaper than its counterparts (geothermal and biomass in this case), forecasting the photovoltaic power production is an essential task for guaranteeing optimal microgrid operation. Day-ahead photovoltaic power forecasts are issued based on solar irradiance forecasts provided by the Global Forecasting System and further integrated into the day-ahead scheduling of the microgrid. This algorithm, together with the cost-optimization algorithms, gives intelligence to the management protocol generating the input parameters (power and operating time for each generating unit) for the microgrid Energy Management Controller. Different cost optimization methods are studied and compared, including mathematical models, such as Mixed Integer Linear Programming, or nature-inspired optimization algorithms, such as Genetic Algorithm, Harmony Search Algorithm, and Particle Swarm Optimization. It is shown that these algorithms provided suitable solutions for the energy balance and scheduling ensuring minimal operation costs for the microgrid. The study also highlights the differences and limitations of these algorithms and shows a method on how these energy management methods can be integrated into a real-world microgrid providing a step forward in the development of intelligent microgrids.

Unit Operation Combination and Flow Distribution Scheme of Water Pump Station System Based on Genetic Algorithm

Pump station engineering is a water conservancy project that utilizes water pump units for energy transfer and conversion, and safe water transportation. This study ensures the safe and efficient operation of the pumping station system by taking a pump station project containing seven water pump units as the research object. Under the premise that the total water intake of the pumping station is certain, the flow distribution of the pumping station is optimized with the working flow and head as the constraint function, and the minimum operating cost as the objective function, and the required number of operating units is determined to obtain the optimal combination of flow distribution and the corresponding working flow of each operating unit. Compared with previous studies, the novel feature of this study is that a genetic algorithm is used to optimize the power-up combination and rationally distribute the flow, which can minimize the energy consumption and maximize the operation efficiency of the pumping station. Then, a genetic-algorithm-based optimization method is constructed for unit operation combination and flow distribution. The optimized combination of units is evaluated by using the uniformity of flow velocity at the inlet section of the water pump and the average deviation angle of the section as indicators. The results indicate that, when each operating unit in the pump station has the same working flow distribution, the total input power of the pump station is at maximum. As the differences increase in the working flow rates allocated to each operating unit, the total input power of the pump station decreases. Through the proposed schemes, the optimal operating combination and flow distribution of the pump station system can be achieved when the total water intake is constant, thereby enabling efficient operations and creating maximum economic and social benefits.

Optimal Sizing of Battery Energy Storage Systems Considering Degradation Effect for Operating and Electricity Cost Minimization in Microgrids

A microgrid is a low‐voltage distribution network designed to provide power for small‐scale and isolated communities consisting of distributed generation and energy storage systems. In order to achieve reliable power and proper energy utilization, energy storage systems plays a vital role in microgrids by storing energy during off peak hours and discharges energy during peak hours. One of the major issues in the isolated microgrids with intermittent nature of distributed generations is the balance of energy demand. This can be achieved by appending renewable energy sources with suitable battery energy storage systems (BESS), to provide the reserve support in meeting the load demand. Battery degradation effect plays a major role in analyzing the performance of BESS lifetime. Battery degradation effect relates the capacity reduction of energy of BESS that is to be delivered to meet the load demand. Therefore, microgrid systems with BESS considering degradation effect should be optimized in such a way as to obtain minimum operating cost while ensuring minimum electricity cost to customers. Simulations of hourly battery discharges rates and simulation of actual discharge rates with obtained simulated rates need to be calculated to determine the degradation effect. The battery degradation cost and lifetime can be calculated correspondingly to minimize the objectives. Correspondingly, herein, an optimization algorithm for microgrid operating cost and customer electricity cost minimization for 24 h time horizon while considering BESS degradation effect by determining kWh and MWh throughput is presented. Particle swarm optimization (PSO), accelerated particle swarm optimization (APSO), Jaya optimization (JAYA) technique, and linear programming interior point algorithm (LP‐IP) are applied to determine the optimal operating cost and electricity cost by simulating BESS degradation parameters. Results obtained using PSO, APSO, JAYA are compared with LP‐IP solver‐based algorithm for BESS lifetime, BESS degradation cost, microgrid operating cost, and customer electricity cost for 24 h.

Network-constrained flexible ramping product provision of prosumer aggregator: a data-driven stochastic bi-level optimization

Prosumers are expected to provide the flexible ramping product (FRP) in the power system. However, voltage violations and line congestion may arise in the distribution network, when FRP delivered by prosumers. Hence, this paper proposes a data-driven stochastic bi-level optimization model to coordinate the prosumer aggregator to decide FRP-offering while ensuring distribution network security under FRP delivery. In the proposed bi-level model, the upper-level is a min-max problem, representing the minimum expected cost under the worst-case scenario probability distribution for the prosumer aggregator. The lower-level is the operation cost minimization within the distribution network security for distribution network operator. The proposed model is converted into a single-level model using the Karush-Kuhn-Tucker condition and strong duality theory, and applied to the modified IEEE 33-bus network with three prosumers. The results demonstrate the effectiveness of the proposed model.

Impact of Carbon Capture (Storage) and Carbon Tax on Economic Dispatch of an Integrated Energy System

Finding the balance between economy and low emission of an integrated energy system (IES) has become one of the current research hotspots. This article introduces the carbon tax and carbon capture (storage) technology within the framework of the IES model. The IES comprises of components such as combined heat and power units equipped with carbon capture facilities, wind power generators, photovoltaic panels, and energy storage systems. The objective function of the model is the minimization of operational costs. Low-carbon economic operation dispatching problem under the constraints of energy conversion, energy balance, and operation cost is studied based on a mix-integer linear programming model. Through sensitivity analysis, this study explores the impact of varying carbon tax levels on the operational costs and emissions of an IES while considering peak and valley price differences. The emission reduction potential of the IES under different policy and technology scenarios is also estimated.

Stochastic uncertainty management in electricity markets with high renewable energy penetration

Abstract The high penetration of renewable energy sources (RESs) in modern power systems poses two conflicting issues. First one is the reduction in the operation costs resulted from RESs utilization instead of the expensive fossil fuel thermal generating units. However, the RESs are characterized by its uncertain and intermittent behavior that their output power is not dispatchable and not known exactly due to forecast errors. Therefore, reserves are scheduled in the day-ahead market to meet the uncertain supply from RESs which add some costs to the system. The decision maker should be aware of those two conflicting objectives in order to operate the system in the optimal way with minimum operation costs. The problem of the market clearing is formulated as Mixed Integer Linear Programming (MILP) problem using GAMS software. It is considered a two stage stochastic programming with the objective of minimizing the expected total operation energy and reserve costs while satisfying the various operational constraints. The results show the effectiveness of the RESs integration in different cases with the consideration of load shedding, RESs curtailment and transmission congestion. As shown from the results, when the network is congested, the operation costs are increased due to the load shedding and RESs curtailment events occur. Moreover, scheduled reserves are increased to face the uncertainty of RESs. As the RESs power penetration increases with no network congestion, a decreasing trend in the operation cost can be seen. However, this trend is less marked as the uncertainty of RESs generation increases. For the network congested case, the reduction rate of the operation cost is decreased for increasing RESs power penetration levels.

An Optimal Energy Control System for Campus Microgrid Using Crow Search Algorithm Considering Economic Dispatch

This article presents an optimal energy control system that considers economic dispatch (ED) for a campus microgrid to reduce its operating cost. A newly developed crow search algorithm (CSA) is used to enforce the ED in this work. To achieve this purpose, an optimal size of distributed energy resources (DERs) in the campus microgrid is assumed. CSA is used to optimize the energy control system and find the minimum operating cost of the campus microgrid. To indicate the effectiveness of CSA, several scenarios under various load demand conditions in grid-connected and stand-alone microgrid modes are investigated in this work. According to the findings, the suggested model is capable of sufficient power supply in all scenarios and reduces the operating costs more effectively than the reference delineated in the same case. The outcomes confirm that the suggested model’s performance is optimal for the energy control system of a campus microgrid.

Warehouses without aisles: Layout design of a multi-deep rack climbing robotic system

In the last decade, different multi-deep rack climbing robotic (MRR) systems have been introduced, particularly in e-commerce warehouses. These systems have great benefits, as aisles are no longer needed, allowing a high storage density on a small footprint. They only need vertical channels through which battery-powered robots can climb the racks, retrieve totes from any desired position, and bring them to a workstation. This paper studies two novel MRR system layouts: the cross and the compact layout. In addition, we compare performance with the more traditional aisle-based layout. The system performance, particularly operational cost and energy consumption, depends on these system layouts. The paper establishes queuing network models to investigate the trade-off between storage capacity and throughput of the system with these three layouts, taking robot blocking prevention into account. We compare the throughput, storage density, horizontal travel time, and energy consumption of the system. The results show that, in most cases, the compact layout outperforms other layouts on throughput. For energy consumption, the choice of layout depends on the footprint. We formulate a model to assist warehouse managers in choosing a layout of minimum annual operational cost, with a required storage and throughput capacity. We also compare the MRR system with an alternative robotic compact storage and retrieval system on operational cost and energy consumption. The MRR system appears to always have lower energy consumption and operational cost.

Vehicle relocation and staff rebalance problem in one-way free-floating car-sharing system: A novel multi-iteration probabilistic greedy algorithm

This research aims to address the staff-based vehicle relocation problem that arises in a one-way car-sharing system operating in a combined mode, in which both on-street parking slots and parking hubs are utilized. The objective of the problem is to determine the relocation plan for vehicles and travel routes for staff with the minimum operational costs. A novel mathematical model is proposed to control the model size, in which the parking spaces constraint is considered without introducing dummy nodes. Then, based on the structure of the model, a heuristic algorithm with tailored operators and the initial solution generation method is proposed to solve the problem. The experimental results demonstrate that, with the tailored operators and solving strategies, the proposed method outperforms commercial solvers and other existing heuristic algorithms in terms of both solving efficiency and solution quality. Furthermore, a group of test cases generated from real cases is utilized to examine the performance of the relocation plan in different scenarios. The results indicate that the overall operation costs of the car-sharing system could be effectively controlled by distributing parking capacities among parking hubs evenly. In addition, the proposed model and high-efficiency algorithm also enable decision-makers to test the performance of the car-sharing system in various scenarios and help them to get a better understanding of the system.

Multiscale analysis and cost optimization of a reversible solid oxide cell system with coordinated degradation and efficiency considering Ni-particle coarsening

In H2-supported microgrids, the electrochemical performance deterioration of reversible solid oxide cell (rSOC) is primarily caused by the coarsening of Ni particles. Specifically, there are still various unresolved questions from microdegradation mechanisms to macroperformance optimization of rSOC systems that require further investigation and solutions. In this study, a novel multiscale-multiphysics field model consisting of a modified Ni coarsening model incorporated into the rSOC system model is used to analyze the effects of operating modes and conditions on degradation behavior. The issue of coordination between degradation and efficiency is settled for the first time by introducing minimization of the total operating cost of an rSOC system. The results indicate that the degradation rate (rdeg) is higher in the fuel cell mode, and this is related to the partial pressure ratio pH2/pH2O within H2-electrode pores. Furthermore, rdeg is found to be most affected by the PEN temperature, followed by the fuel utilization and fuel molar fraction. The minimum total cost is approximately 0.49 $/kWh taken at a low system intake (including H2O, H2, and air) with higher temperatures. The optimization weight shifts gradually toward efficiency owing to the higher gas supply cost. These findings provide a valuable reference for the selection of the optimal operating state for the rSOC system considering degradation.

Applications of chaotic quantum adaptive satin bower bird optimizer algorithm in berth-tugboat-quay crane allocation optimization

With the advent of the post-pandemic era, the global economic situation has tightened, and the economic benefits of container ports have also been affected. This paper attempts to build a novel berth-tugboat-quay crane joint scheduling method for container ports to reduce the economic cost of container ports. Firstly, from the perspective of the economic cost of container ports, considering random economic factors, this paper proposes a new berth-tugboat-quay crane joint dispatch optimization model, namely E-BUQC, by taking the minimum time cost of vessels in port, the cost of additional freight distance for trucks, the cost of tugboat operation, and the minimum operation cost of quay cranes as the objective function. Then, in order to obtain a relatively high-quality solution, based on chaotic mapping and quantum computing, this paper constructs a new chaotic quantum adaptive satin bower bird optimizer algorithm, namely CQASBO, formulates exclusive coding rules for the E-BUQC model, develops a feasible integer algorithm, and proposes a new joint scheduling solution method of berth-tugboat-quay crane, named E-BUQC_CQASBO. Subsequently, based on the simulation examples of two ports in northern China, the new solution method is tested. The simulation test results show that the E-BUQC model obtains an appropriate solution; The CQASBO constructed in this paper has better performance than other traditional comparison algorithms selected in this paper and can provide a better solution when solving the E-BUQC model.

Research on Energy Management Strategy for GCPBS

Integrating solar power into the power grid is significant for the rapid acceleration of energy transition. As a result, it is urgent to make appropriate operational strategies for grid-connected photovoltaic battery systems (GCPBS). At present, a variety of energy management strategies have been proposed, while the influence of battery degradation and energy flow distribution hasn’t been considered, which also makes a huge difference to grid-connected photovoltaic-battery system operation. Therefore, a novel energy management strategy is proposed in this paper to provide optimal energy utilization. Moreover, a case study using data from California is utilized to investigate the performance of the proposed method, where a widely used operation strategy is utilized to be compared. Results prove that the proposed method can control flexibly energy flow to achieve the minimum cost of GCPBS operation with reasonable battery charging and discharging process, and the battery is charged at tough prices and discharged at high prices, where coupling effects of all hours and price are considered and the battery is fully utilized.

Multi-Source Energy Storage Stations Control Strategy Considering Implicit Linearization of The Power Flow Manifold

With the development of distributed new energy and multi-type loads, in order to realize the effective management of distributed power sources by multi-microgrids and better play the supporting role of microgrids on distributed power sources, we consider the dynamic changes of power flow in the distribution network caused by the power interaction between multi-microgrids and shared energy storage and propose an optimal dispatching strategy of multi-microgrids considering multi-source synergy considering the constraints of power flow. Since the constraint of the distribution network is a highly nonlinear mixed integer programming problem with power terms, a linearized approximation model is adopted for the power flow of the distribution network, and then a multi-objective model with minimal wind power and photovoltaic abandonment and minimum total operating cost of multi-microgrid is established. The improved IEEE18 node system is used to verify the model, and the results show that the proposed method comprehensively considers the linkage of grid-load-storage, which makes the dispatching results more realistic and more applicable.

Managing Water-Energy-Carbon Nexus for Urban Areas With Ambiguous Moment Information

The rapid growth of electric load fuels urbanization but creates unsettling environmental challenges. In that regard, the energy system is a crucial part of consuming water and emitting greenhouse gases. To support reliable and economical energy system operations, with reduced water waste and carbon emissions, we design a viable co-optimization of water-energy-carbon nexus for an integrated energy system by adequately modelling essential interdependencies of power, gas, and water systems. A two-stage distributionally robust optimization method is proposed: the first-stage model minimizes the day-ahead reserve capacity scheduling for the next day, and the second-stage model enables real-time dispatch by considering the variability in renewable power generation. The carbon emissions by power generation and distribution are incorporated in both stages to ensure low-carbon operations. We use a two-stage moment-based distributionally robust approach to capture and represent the uncertainties in renewable generation. Instead of assuming a deterministic distribution, we characterize an ambiguity set with mean vectors and covariance matrices, which produces a family set of distributions. We conduct case studies using a modified IEEE 33-bus power system that includes and links a 20-node gas system and a 10-node water system. The comparative results indicate that the proposed co-optimization of water-energy-carbon nexus is superior than several benchmarks. By considering the close interdependencies of water, energy, and carbon, we create an optimal resource allocation scheme, which is capable of minimizing operation costs, lessening carbon emissions, and reducing water waste. The current research enables effective operation schemes for urban energy systems, illustrates a viable way to curb local environmental emissions, and increases water usage efficiency in the water-energy-carbon nexus.

Electrochemical degradation of ciprofloxacin through a DoE-driven optimization in a filter-press type reactor under batch recirculation mode.

In this work, the electrochemical degradation of ciprofloxacin (CIP) was studied in a filter-press-type reactor without division in a batch recirculation manner. For this purpose, two boron-doped diamond (BDD) electrodes (as cathode and anode) were employed. Also, the optimal operating conditions were found by response surface methodology (RSM) following a central composite face-centered design with three factors, namely current intensity (i), initial pH (pH0), and initial concentration ([C]0) with two responses, namely remotion efficiency (η) and operating cost. Optimal operating conditions were i = 3 A, pH0 = 8.49, and [C]0 = 33.26 mg L-1 within an electrolysis time of 5 h, leading to a maximum removal efficiency of 93.49% with a minimum operating cost of $0.013 USD L-1. Also, a TOC analysis shows an 80% of mineralization extent with an energy consumption of 5.11 kWh g-1 TOC. Furthermore, the CIP degradation progress was followed by mass spectrometry (LC/MS) and a degradation pathway is proposed.

Multi-Objective Operational Cost Management with Minimum Net Emission of a Smart Microgrid

This research article considers a microgrid (MG) with various distributed generation sources (DGs) such as microturbine (MT), fuel cell (FC), wind turbine (WT), and solar cell (PV) along with the battery storage system. This article aims to simultaneously minimize operational costs and emissions for a daily schedule, while considering uncertainties such as load demand, market prices, and electricity generated by different renewable energy sources (RESs). This article proposes the slime mould algorithm (SMA) to solve this complex optimization problem. Initially, SMA is tested for three scenarios considering the minimization of operational cost and emission, respectively, as two single objectives. In Scenario A, a reduction of 1.95% in operational cost and 4.57% in emissions is observed compared to the Grasshopper Optimization Algorithm (GOA). In scenarios B and C, SMA reduces operational cost by 0.78% and 2.65%, respectively, while also reducing emissions by 6.27% and 0.28%, respectively, when compared to GOA. The results obtained by other algorithms are worse than those obtained by GOA. This study also showed that SMA effectively considers multi-objective functions by using the weighted sum method and the fuzzy decision-maker to approach the Pareto-optimal solution, achieving a good tradeoff between operational cost and emissions in each scenario.

Scheming preventive maintenance assignments for mass rapid transit (MRT): Taoyuan Metro MRT system

Mass rapid transit (MRT) maintenance systems enable owners to make optimal decisions for site selection and preventive maintenance plans while considering the total operating costs. To ensure that the MRT planning system is effective, it is essential to construct a maintenance cost plan. This study implements the mathematical planning method to establish a comprehensive plan for route maintenance and preventive maintenance for the Taoyuan MRT based on the minimum total operating cost as the target, and meets the practical emergency repair time limit, site location distance and other relevant constraints. This study developed and evaluated novel heuristic algorithms with CPLEX software. Sensitivity analysis was also conducted to test the model’s effectiveness. The proposed model establishes the optimum maintenance cost and finds the critical parameters that affect overall cost within 1% error, maintaining service quality. The proposed model is an accurate tool for engineers to reduce time-consuming procedures.

Scheduling optimization of a wind power-containing power system considering the integrated and flexible carbon capture power plant and P2G equipment under demand response and reward and punishment ladder-type carbon trading

At present, coal-fired thermal power still dominates in China, and as the environmental pollution problem worsens, it is critical to find the optimal combination of coal and new energy to reduce CO2 emissions and promote new energy consumption. Based on this, this paper considers a reward and punishment ladder-type carbon trading mechanism, firstly, unit transformation of the conventional coal power plant (CPP) on the power side, forming the integrated and flexible carbon capture power plant (IFCCPP), and constructing a coupling model of the IFCCPP with power to gas (P2G) equipment and wind farms(WFs), forming an IFCCPP-P2G-WF system; secondly, introducing a price demand response mechanism on the load side, and establishing a two-stage scheduling model for the IFCCPP-P2G-WF system, with the first stage model aiming at the minimum sum of squared load fluctuations and the second stage model aiming at the minimum operation cost. Finally, the effectiveness of the scheduling model proposed in this paper is validated by comparing and analyzing the scheduling results under eight scenarios. When compared to other models, the proposed model significantly increases wind power consumption, reduces carbon emissions, and lowers operation cost, all of which have advantages.

Low-Carbon Economic Optimization of Integrated Energy System Considering Refined Utilization of Hydrogen Energy and Generalized Energy Storage

In order to improve the level of new energy consumption in the system and utilize the clean and efficient characteristics of hydrogen energy, an integrated energy system (IES) scheduling model considering refined utilization of hydrogen energy and generalized energy storage is proposed. Firstly, the two-stage hydrogen energy utilization model of power-to-gas (P2G) is finely modeled, and the waste heat of the P2G methanation reaction is innovatively coupled with the Kalina cycle to improve the thermoelectric decoupling capability of the combined heat and power (CHP) unit. Secondly, integrated demand response, electric vehicles, and hydrogen-containing multi-source energy storage equipment are used as generalized energy storage resources to cut peaks and fill valleys. Then, on the basis of considering the ladder-type carbon trading mechanism, the IES conventional operation model is constructed with the minimum operating cost of the system as the objective function. Furthermore, considering the source-load uncertainty of IES operation, a multi-energy complementary optimal scheduling model of hydrogen-containing IES based on conditional value-at-risk was established. Through simulation analysis, it can be seen that the proposed model takes into account both economic and environmental benefits and improves the system’s ability to “peak cutting and valley filling” and measure risk levels.