Multi-objective Optimization System Research Articles

An Integrated Process Planning and Scheduling problem solved from an adaptive multi-objective perspective

The planning and scheduling of processes are the main foundations of modern manufacturing systems, and their efficient integration represents one of the principal interests in operations research. Most of the reported literature in operations research formulates the optimization process considering a single objective. However, real-world manufacturing systems are influenced by several variables that must be considered in a complete process planning task. To solve this gap, this paper presents a computational study where the main variables of process planning are integrated: production times, production costs, and the Makespan of the system. To incorporate such variables, the problem is reformulated as a multi-objective optimization system by employing an Adapted Non-dominated Sorting Genetic Algorithm ii (ANSGA-ii) methodology. Under such an approach, the ANSGA-ii codifies the process planning elements into its structure to execute the operators for determining the best tradeoff between objectives. In this work, different cases of study are considered and compared with some of the most employed multi-objective approaches in the literature to demonstrate the performance and robustness of the proposed method. Statistical analysis corroborates that the proposed ANSGA-ii can generate competitive results outperforming the techniques employed in the experimental study.

Optimization of Hydraulic Efficiency and Internal Flow Characteristics of a Multi-Stage Pump Using RBF Neural Network

In order to improve the hydraulic efficiency and internal flow pattern of a multi-stage pump under multiple flow conditions, an intelligent optimization design was proposed for its hydraulic components. Sensitivity analysis was used to select the key parameters influencing the hydraulic efficiency of a multi-stage pump. The optimal Latin hypercube sampling and non-dominated sorting genetic algorithm Ⅱ were employed to build a multi-objective optimization system. Moreover, a radial basis function neural network was adopted as the surrogate model of hydraulic efficiency. The research results showed that the impeller outlet width, impeller blade wrap angle, impeller outlet blade angle, and diffuser inlet width were the key factors affecting the hydraulic efficiency. The efficiency of the optimized model increased by 4.35% under the design condition and the matching of the internal flow between the optimized impeller and diffuser was significantly enhanced under the nominal condition. The improved flow pattern could be clearly observed in the flow passage of both the pump impeller and the diffuser. After optimization, the wear performance of the model was also improved compared to the original design. The wear area decreased in size and was distributed more evenly, resulting in a noticeable decrease in the maximum amount of wear.

Thermodynamic analysis and multi-objective optimization of an ORC-based solar-natural gas driven trigeneration system for a residential area

Regarding the growing demands for energy, depletion of fossil fuel resources, and the adverse environmental consequences associated with their utilization, especially global warming, it is imperative to increase the share of renewable resources within energy systems. During the summer season, the residential sector in Khuzestan province, Iran, experiences a significant surge in energy consumption. This elevated demand is mostly attributed to the region's severe temperatures, requiring the extensive use of cooling equipment. As a result, taking into account the high potential for solar energy in the nation, this research first uses TRNSYS software to calculate the cooling and heating loads of a residential area in the province of Khuzestan. Next, a solar-natural gas Combined Cooling, Heating and Power (CCHP) system is designed and modeled using Engineering Equation Solver (EES) software, with the goal of supplying the cooling load. Then, using the particle swarm optimization technique, the system's multi-objective optimization was completed. The results showed that a fuel consumption rate of 0.04274 kg/s is required in steady state conditions to supply a cooling load of 1446.2 kW for the region with 1000 m2 of solar collecting area. A total of 465.7 kW of electric power and 3336 kW of heating are produced. The system's energy and exergy efficiencies were determined to be 186.6 % and 27.81 %, respectively. Eventually, the findings of this study demonstrated that the proposed configuration has the potential to provide multiple positive outcomes with a high level of efficiency, and improve the share of renewable energy.

Multidisciplinary Design Methodology for Micro-Gas-Turbines—Part II: System Analysis and Optimization

Abstract Owing to their high specific energy capabilities, ultramicrogas turbines (UMGT) are a high-potential technology to provide portable electric power supply for applications with demand of less than 1 kW. UMGT conceptual design is challenged by small-scale effects augmenting interdisciplinary dependencies leading to highly coupled, nonlinear component interactions. This work provides a novel approach to conceptual UMGT design by combining reduced order component and system modeling with constrained multi-objective optimization. Hereby, Part I presents integrated design and performance modeling of compressor, turbine, combustor, and generator. In Part II, the heat engine and generator modules are merged into a system framework by establishing conceptual UMGT rotor geometry and engine design. Following bearing selection and lifetime assessment, experimentally validated reduced order models are developed for heat transfer and rotordynamic analysis. Using the elaborated framework, a constrained multi-objective system optimization of a 300 W engine is performed based on ten design parameters and comparing SiAlON and Inconel 718 as potential rotor materials available for additive manufacturing. Hereby, bearing lifetime, system efficiency, and specific power are maximized while meeting rotordynamic, structural, and thermal requirements. Evaluating the results, interdisciplinary effects are highlighted, and two optimum engine configurations are suggested.

Using different Heuristic strategies and an adaptive Neuro-Fuzzy inference system for multi-objective optimization of Hybrid Nanofluid to provide an efficient thermal behavior

The importance of multi-objective optimization in hybrid nanofluid research lies in its wide-ranging applications across fields such as microelectronics, aerospace, and renewable energy. These specialized fluids hold the potential to elevate the performance and efficiency of diverse systems through enhanced heat transfer capabilities. This research endeavor is centered around optimizing a hybrid nanofluid composed of Silicon Oxide-MWCNT-Alumina/Water by leveraging a mix of heuristic approaches and an adaptive neuro-fuzzy inference system. To this end, the most influential set of input parameters has been identified using four state-of-the-art algorithms: Non-dominated Genetic Algorithm, multi-objective particle swarm optimization, Strength Pareto Evolutionary Algorithm 2, and Pareto Envelope-based Selection Algorithm 2. The goal of the optimization process is to modify the temperature (T = 20 °C to 60 °C) and the volume fraction of nanoparticles (SVF=0.1 % to 0.5 %). Finding the optimal combination of these parameters that results in the hybrid nanofluid with the maximum thermal conductivity (knf) and the lowest dynamic viscosity is the main objective. The findings of this research have the potential to drastically improve the performance of systems in a variety of applications and to change the creation of sophisticated, high-efficiency heat transfer fluids.

Strategy and capacity optimization of renewable hybrid combined cooling, heating and power system with multiple energy storage

Combined cooling, heating, and power systems offer significant potential for integration with renewable energy sources, such as solar and geothermal energy, alongside energy storage devices. However, the effectiveness and feasibility of these systems depend crucially on the operational strategies and capacity planning for each component. Therefore, an innovative hourly dynamic simulation model that integrates solar energy, geothermal energy, and multiple energy storage devices is established. Using the multi-objective comprehensive evaluation method that combines Technique for Order Preference by Similarity to Ideal Solution methods and non-dominated sorting genetic algorithm-II including elite strategy, establish five dimensions of multi-objective optimization model, including energy-saving, economy, environmental protection, system independence and renewable energy utilization scale. Meanwhile, the system and reference system in a community in Beijing are used as models to analyze the equipment capacity under the three operating strategies. The findings reveal that, compared to the reference system, the new system attains optimal performance when following the electrical load strategy. Its comprehensive index reaches 69%, surpassing the thermal load and hybrid load strategies by 8% and 11%, respectively. When the target is set as “independence-renewable energy-cost,” the utilization rate of solar energy and geothermal energy reaches an impressive 79%, showcasing excellent renewable energy utilization. The presented model holds substantial significance for advancing research in the realms of operating strategies and equipment capacity planning within such systems.

Simulation of lake system based on multi-objective optimization algorithm and system dynamics model

Simulation of lake system based on multi-objective optimization algorithm and system dynamics model

Aerothermal optimization of turbine cascade squealer tip with non-uniform squealer height

The squealer tip has significant influence on both the aerodynamic and heat transfer characteristics of the high-pressure turbine blade. However, due to the complexity of parameterization and meshing of the squealer and the complicated flow structure within the over-tip region, the existing squealer designs in the open literature have constant squealer heights. In this paper, the design space to the squealer height with non-uniform squealer height is extended and the new flow features it may bring are investigated. A parameterization system specifically designed for the non-uniform squealer height using five control parameters is implemented to automatically generate the geometry and hybrid meshes. Combining it with the multi-objective optimization system using genetic algorithms, a transonic turbine cascade squealer tip is optimized employing Reynolds-averaged Navier–Stokes k–ω shear stress transport model. The main objective of this study is to obtain a squealer configuration with the lowest total pressure loss coefficient and heat transfer coefficient. The optimum configuration with non-uniform squealer height achieves improvements in both the aerodynamic efficiency and the heat transfer performance, relative to the baseline conventional squealer tip geometry with the constant squealer height. Additionally, this work demonstrates that a flow structure in which the main flow forms a “blanket” below the leakage flow in the squealer is beneficial for aerothermal performance, especially reducing heat transfer losses, which provides valuable insight into the squealer tip design of advanced high-pressure turbines.

A Cogeneration-Coupled energy storage system utilizing hydrogen and methane-fueled CAES and ORC with ambient temperature consideration enhanced by artificial neural Network, and Multi-Objective optimization

The increasing demand for energy in both commercial and residential sectors poses a pressing challenge, driving the ongoing quest for sustainable and virtually limitless energy resources. As nations become increasingly reliant on energy, the need to enhance energy system efficiency while curbing costs and reducing greenhouse gas emissions, particularly CO2 from fossil fuel combustion, becomes imperative. This paper presents a thermodynamics modeling, economic assessment, and optimization of a multigenerational module that utilizes compressed-air energy storing and polymer electrolyte membrane electrolysis with methane and hydrogen fuels, with a case study focused on Munich, Germany. Using EES, a system comprising of a modified organic Rankine cycle, gas turbine, PEM electrolyzer, and CAES unit was modeled. The combustion chamber was used to exploit methane and a combination of methane and hydrogen. The performance of the suggested module under these two scenarios was evaluated using multi-objective optimization to select the optimal scenario based on TOPSIS. In this study, we conducted 700 runs and obtained data on seven inputs. To derive a mathematical function from this data, we employed an Artificial Neural Network (ANN). This function was subsequently integrated into a multi-objective optimization system, allowing us to optimize the desired objectives. It was determined that Scenario 1 was the best choice for lowering the cost rate, reducing CO2 emissions, and optimizing energy round-trip effectiveness (ERTE). The highest exergy destruction rates were observed in the gas turbine, combustion chamber, and recuperator of the suggested system. The cost rates for ORC, CAES, and PEM electrolyzer were the highest according to the economic evaluation of the proposed module. Moreover, the gas turbine had the highest price. The study assessed the power demand of a building and evaluated the efficiency of the proposed power supply system. Additionally, an investigation was conducted in Munich, Germany, to examine how the system's performance is influenced by daily changes the annual variation in the environment temperature. The results revealed that the exergy destruction rates for the charging and discharging units were identified as 1706.38 kWh and 3019.31 kWh, respectively. additionally, based on the economic analysis, the total cost rate for the proposed energy production system was projected to be 68.073 USD per hour. The subsystems were ranked in the following order of costs: CAES > ORC > PEM electrolyzer. Additionally, within the specified system equipment, the gas turbine equipment exhibited the highest cost rate.

Comparative study on the improvement of a distributed energy system performance by optimized operation strategy from design to operation

The operation strategy directly affects the optimal design and operation performance of distributed energy systems (DESs). Following electrical load (FEL) and following thermal load (FTL) strategies, for example, cannot achieve the flexible scheduling of DESs. As a result, this paper proposes an optimized operation strategy with the optimization objectives of minimizing operating costs and reducing carbon emissions. A multi-objective system optimization model with a minimum total cost and a minimum fossil energy consumption as optimization objectives are established based on the optimized operation strategy. The design and operation of the proposed DES are optimized according to loads of a public building in Changsha, and the results are compared with those under the FEL and FTL strategies. The study demonstrates that the optimized operation strategy can avoid using the gas boiler under the design conditions, which improves the system’s operating efficiency and lowers the total system cost and fossil energy consumption. Compared to the FEL and FTL strategies, the total cost reduction rate of the system under the optimized operation strategy is 1.44% and 1.69%, respectively, and the fossil energy saving rate is 3.25% and 2.3%. Different from the design condition is that the optimized operation strategy under winter and summer operating conditions can not only improve the operating efficiency but also avoid the waste of waste heat from CHP units and reduce the grid power consumption, which makes the total system cost and fossil energy consumption under the optimized operation strategy significantly smaller than those under the FEL and FTL strategies, especially than the FEL strategy. Under the summer operation condition, the optimized operation strategy’s fossil energy saving rate and total cost reduction rate are 6.76% and 12.01%, while the rates are 22.38% and 36.20% in winter. Even if the energy price fluctuates, the total system cost and fossil energy consumption under the optimized operation strategy are less than those under the FEL and FTL strategies. Therefore, the proposed optimized operation strategy can improve the system’s energy efficiency and reduce total cost under the design and operation conditions.

Study of a hull form optimization system based on a Gaussian process regression algorithm and an adaptive sampling strategy, Part II: Multi-objective optimization

Ships have a wide variety of performance specifications and experience complex navigation conditions. Thus, an efficient numerical optimization system for general hull forms must be able to deal with multi-objective optimization problems. Based on a Gaussian process regression (GPR) algorithm and an adaptive sampling strategy, the authors have developed a single-objective optimization system, SBO-MSE + LCB, which is more efficient than the conventional simulation-based design (SBD) optimization system. However, the application of this system to hull form multi-objective optimization problems presents problems such as excessive new samplings, insufficient exploration of new sample points, and unsuitable convergence criteria. To solve the above problems, we first propose an adaptive sampling strategy based on the idea of spatial clustering. The strategy not only effectively controls the number of sampling numbers at each iteration in the SBO-MSE + LCB optimization system but also enables new sample points to achieve the multi-objective efficient exploration of optimal solution sets for optimization. Then, from the perspective of the convergence and diversity of Pareto optimal solution sets, we design adaptive sampling convergence criteria suitable for multi-objective optimization problems. Based on this, we enable the SBO-MSE + LCB optimization system to solve multi-objective optimization problems and verify its applicability with several cases for which the multi-objective mathematical optimization is performed. Finally, we use the SBO-MSE + LCB multi-objective optimization system and the SBD optimization system to optimize the total resistance coefficient of a deep-sea aquaculture vessel at the scantling and ballast drafts. The results show that the optimization efficiency of the SBO-MSE + LCB multi-objective optimization system is improved by 33.33% from the SBD optimization system for similar optimization performance, proving its advantage in efficiency in practical applications.

Urban population prediction based on multi-objective lioness optimization algorithm and system dynamics model

Population size is closely related to economic and social development and change. It is one of the primary and essential elements of overall urban development planning to formulate a population development strategy scientifically through population projections. Therefore, we propose an urban population prediction model based on a multi-objective lioness optimization algorithm and system dynamics. The multi-objective lioness optimization algorithm is used to optimize some critical parameters of the system dynamics model to reduce the subjectivity of the model construction. Taking Xi’an as an example, the validity of the model is verified, and the population size of Xi’an from 2019 to 2050 is predicted by the model. In addition, the impact of different policies and their combinations on the future population is discussed through simulations of three scenarios composed of five policy factors: birth, employment, science and technology, healthcare and education. The results show that the total population of Xi’an will peak at 147,939,242 in 2040, based on current development trends. Moreover, the five policies with the largest to smallest positive effect on population size are: employment policy, fertility policy, education policy, science and technology policy, and health policy, with employment and fertility policies having significantly larger effects than the other three. Therefore, the employment policy and the birth policy are the two most effective policies to promote population growth, and the coordinated implementation of the five policies is the fastest way to increase population size.

Validating Dynamic Sectorization for Air Traffic Control Due to Climate Sensitive Areas: Designing Effective Air Traffic Control Strategies

Dynamic sectorization is a powerful possibility to balance the controller workload with respect to traffic flows changing over time. A multi-objective optimization system analyzes the traffic flow over time and determines suitable time-dependent sectorizations. Our dynamic sectorization system is integrated into a radar display as part of a working environment for air traffic controllers. A use case defining climate-sensitive areas leads to changes in traffic flows. When using the system, three controllers are assessed in two scenarios: the developed controller assistance system and the work in a dynamic airspace sectorization environment. We performed a concept validation in which we evaluated how controllers cope with sectors adapting to the traffic flow. The solution was rated as highly applicable by the involved controllers. The trials revealed the necessity to adapt the current procedures and define new aspects more precisely. In this paper, we present the developed environment and the theoretical background as well as the traffic scenarios. Furthermore, we describe the integration in an Air Traffic Management (ATM) environment and the questionnaires developed to assess the functionality of the dynamic sectorization approach. Finally, we present a proposal to enhance controller guidelines in order to cope with situations emerging from dynamic sectorizations, including naming conventions and phraseology.

Hybrid-integer algorithm for a multi-objective optimal home energy management system

AbstractMost of the energy produced in the world is consumed by commercial and residential buildings. With the growth in the global economy and world demographics, this energy demand has become increasingly important. This has led to higher unit electricity prices, frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management. This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing. This simultaneously reduces the electricity bill and the peaks, while maintaining user comfort in terms of the operating waiting time of appliances. The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming, which involves constraints and consumer preferences. For optimal scheduling, the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm. Two techniques are proposed to manage the trade-off between the conflicting objectives. The first technique is the Pareto-optimal solutions classification using supervised learning methods. The second technique is called the lexicographic method. The simulations were performed based on residential building energy consumption, time-of-use pricing (TOU) and critical peak pricing (CPP). The algorithms were implemented in Python. The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio (PAR) by 28% and 49.32%, respectively, for the TOU tariff rate, and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.

Digital Twin of HVAC system (HVACDT) for multiobjective optimization of energy consumption and thermal comfort based on BIM framework with ANN-MOGA

ABSTRACT This study proposes a novel Digital Twin framework of heating, ventilation, and air conditioning (HVACDT) system to reduce energy consumption while increasing thermal comfort. The framework is developed to help the facility managers better understand the building operation to enhance the HVAC system function. The Digital Twin framework is based on Building Information Modelling (BIM) combined with a newly created plug-in to receive real-time sensor data as well as thermal comfort and optimization process through Matlab programming. In order to determine if the suggested framework is practical, data were collected from a Norwegian office building between August 2019 and October 2021 and used to test the framework. An artificial neural network (ANN) in a Simulink model and a multiobjective genetic algorithm (MOGA) are then used to improve the HVAC system. The HVAC system is comprised of air distributors, cooling units, heating units, pressure regulators, valves, air gates, and fans, among other components. In this context, several characteristics, such as temperatures, pressure, airflow, cooling and heating operation control, and other factors are considered as decision variables. In order to determine objective functions, the predicted percentage of dissatisfied (PPD) and the HVAC energy usage are both calculated. As a result, ANN's decision variables and objective function correlated well. Furthermore, MOGA presents different design factors that can be used to obtain the best possible solution in terms of thermal comfort and energy usage. The results show that the average cooling energy savings for four days in summer is roughly 13.2%, and 10.8% for the three summer months (June, July, and August), keeping the PPD under 10%. Finally, compared to traditional approaches, the HVACDT framework displays a higher level of automation in terms of data management.

Multi-objective optimal regulation model and system based on whole plant photosynthesis and light use efficiency of lettuce

Lettuce growth and light energy consumption in a plant factory with artificial lighting (PFAL) were studied, and whole plant photosynthetic rate (ACO2) and light use efficiency (LUE) data were obtained on different days after planting (DAP) under different photosynthetic photon flux densities (PPFDs). Genetic algorithm‐support vector regression (GA-SVR) was used to construct the ACO2 and LUE prediction models. The coefficient of determination (R2) between the predicted and measured values of the ACO2 model was 0.97 and the root mean square error (RMSE) was 0.42 μmol·mol−1·plant−1·min−1, and R2 between the predicted and measured values of the LUE model was 0.97 and RMSE was 0.36%. The ACO2 and LUE prediction models were used as the objective functions, and multi-objective search was performed by the non-dominated sorting genetic algorithm II (NSGA-II) and the distance-based knee point detection method were used to obtained the optimal equilibrium solution for different DAPs. The optimal equilibrium solutions were used as the basis to establish the light regulation model based on lettuce DAP with R2 of 0.99. To validate the effect of model regulation, a lettuce light regulation system was built using an artificial climate chamber for a 30-day system validation. The results showed that compared with the traditional quantitative light supplementation method, the dry matter of model regulation significantly increased by 39.23% and 29.48% compared with quantitative PPFD150 (μmol·m−2·s−1) and PPFD200 (μmol·m−2·s−1). Model regulation increased the number of total light quanta consumed by 1.39% over PPFD150 and decreased by 23.96% over PPFD200; however, plant productivity increased by 35.35% and 33.14%, respectively. Model regulation significantly reduced the number of light quanta consumed per unit mass of lettuce production by 24.35% for PPFD150 and 41.54% for PPFD200, and LUE of light-emitting diode energy into dry matter was significantly increased by 33.49% and 75.09%. Therefore, the light regulation model based on multi-objective optimization in this study could improve crop yield and increase LUE.

An immune inspired multi-agent system for dynamic multi-objective optimization

In this research, an immune inspired multi-agent system (IMAS) is proposed to solve optimization problems in dynamic and multi-objective environments. The proposed IMAS uses artificial immune system metaphors to shape the local behaviors of agents to detect environmental changes, generate Pareto optimal solutions, and react to the dynamics of the problem environment. Apart from that, agents enhance their adaptive capacity in dealing with environmental changes to find the global optimum, with a hierarchical structure without any central control. This study used a combination of diversity-, multi-population- and memory-based approaches to perform better in multi-objective environments with severe and frequent changes. The proposed IMAS is compared with six state-of-the-art algorithms on various benchmark problems. The results indicate its superiority in many of the experiments.

Towards Optimal Planning for Green, Smart, and Semantically Enriched Cultural Tours

This concept paper presents our viewpoint regarding the exploitation of cutting-edge technologies for the delivery of smart tourism cultural tours. Specifically, the paper reports preliminary work on the design of a novel smart tourism solution tailored to a multiobjective optimization system based on factors such as the preferences and constraints of the tourist/visitor, the city’s accessibility and traffic, the weather conditions, and others. By optimizing cultural tours and delivering comfortable, easy-to-follow, green, acceptable visiting experiences, the proposed solution, namely, OptiTours, aims to become a leading actor in tourism industry transformation. Moreover, specific actions, applications, and methodologies target increasing touring acceptance while advancing the overall (smart) city impression. OptiTours aims to deliver a novel system to attract visitors and guide them to enjoy a city’s possible points of interest, achieving high visitor acceptance. Advanced technologies in semantic trajectories’ management and optimization in route planning will be exploited towards the discovery of optimal, smart, green, and comfortable routes/tours. A novel multiscale and multifactor optimization system aims to deliver not only optimal personalized routes but also alternative routes, ranked based on visitors’ preferences and constraints. In this concept paper, we contribute a detailed description of the OptiTours approach for ICT-based smart tourism, and a high-level architectural design of the solution that is planned to be implemented in the near future.

Product Design Concept Analysis Using Grey Relational Analysis

In this from analysis GRA method is the most ideal solution Short-distance and negative-best The solution with the longest distance from the solution Determines, but the comparison of these distances Does not consider importance. From the result it is seen that Design 2 is got the first rank where as is the Design 5 is having the lowest rank. The definition of product design is that of solving users’ troubles or in a given marketplace Imagine, creates and recreates products that cope with particular desires Describes implementation. The key to a success product design is give up consumer Understanding the client is the individual for whom the product is made. Of product layout Definition, solving users' issues or addressing particular wishes in a given marketplace Describes imagining, growing and re-enforcing making products. The key to a success product design knows the stop-person customer, for whom the product is the man or woman who's being created. The MOORA technique turned into first added by means of the Multi-cause optimization technique, which solves a number of complicated selection-making issues in the manufacturing surroundings Can be used efficiently. Multi-Objective Optimization System Ratio Analysis (MOORA) One of the MADM techniques is the sort of potential college students. Alternative: Design 1, Design 2, Design 3, Design 4, Design 5. Rating Option: Expected mechanical safety, wear size, operating and Maintenance cost, high load balance of a machine. Product design concept from the result it is seen that Design 2 and is got the first rank whereas is the Design 5 got is having the lowest rank.

Thermo-economic analysis and multi-objective optimization of a reversible heat pump-organic Rankine cycle power system for energy storage

The rapid growth in renewable energy has reinforced the efforts to develop new scalable energy storage to balance the mismatch between energy production and demand. The reversible heat pump-organic Rankine cycle (HP-ORC) Carnot battery system is a possible candidate for this. Further investment cost reduction and the utilization of low-temperature energy were extremely beneficial to its development and application. A low-temperature and simple reversible HP-ORC storage system based on a compression and expansion dual-function unit was developed in this paper. A screening of 3 candidate working fluids was performed and R1233zd (E) was selected as the final candidate for the demonstrator. A mathematical model of the presented system was built to analyze the energy, exergy, and economic performance. Additionally, a multi-objective optimization of the system was carried out to find the optimal storage temperature taking economic and energy criteria into account. Finally, exergy analysis was performed under the optimal condition. The results show that the higher isentropic efficiency and temperature of the heat source are, the better the performance of the system is. When the optimal upper and lower storage temperatures are 126 ℃ and 99 ℃, the power-to-power efficiency (P2P), electrical storage capacity (ESC), levelized cost of storage (LCOS), and exergy efficiency are 28.16 %, 1.77 kW∙h/t, 0.36 $/kW∙h, and 61.82 %, respectively. The condenser and evaporator need to be improved in terms of their large exergy loss rate and low exergy efficiency.