Minimum Energy Cost Research Articles

ИНСТРУМЕНТЫ ЦИФРОВИЗАЦИИ В ПРАКТИКЕ ПРОЕКТИРОВАНИЯ ПРИ РЕКОНСТРУКЦИИ ПОДЗЕМНЫХ ТРУБОПРОВОДОВ БЕСТРАНШЕЙНЫМИ МЕТОДАМИ

The article focuses on the possibilities of multi-optional design of repair and rehabilitation works on dilapidated pressure pipeline networks with the use of automated complexes in order to achieve minimum energy costs in water transportation with regard to the temperature factor. Two automated programs based on hydraulic and energy calculation operations are presented as digital tools. As a basic variant of reconstruction of dilapidated pipelines, the trenchless Swagelining technology was accepted. It is realized by pulling pre-compressed polymer pipes into old pipelines with their subsequent straightening. The paper presents information on regulatory and technical documentation and the application of advanced technology, installation and operation conditions, as well as comparative assessment of energy consumption after reconstruction of dilapidated sections of pipelines with polymer pipes with the possibility of minimizing energy consumption during water transportation under non-isothermal conditions of pipeline operation.

Comparison of Energy Storage Management Techniques for a Grid-Connected PV- and Battery-Supplied Residential System

The use of renewable energy sources (RES) such as wind and solar power is increasing rapidly to meet growing electricity demand. However, the intermittent nature of RES poses a challenge to grid stability. Energy storage (ES) technologies offer a solution by adding flexibility to the system. With the emergence of distributed energy resources (DERs) and the transition to prosumer-based electricity systems, energy management systems (EMSs) have become crucial to coordinate the operation of different devices and optimize system efficiency and functionality. This paper presents an EMS for a residential photovoltaic (PV) and battery system that addresses two different functionalities: energy cost minimization, and self-consumption maximization. The proposed EMS takes into account the operational requirements of the devices and their lower-level controllers. A genetic algorithm (GA) is used to solve the optimization problems, ensuring a desired State of Charge (SOC) at the end of the day based on the next day forecast, without discretizing the SOC transitions allowing a continuous search space. The importance of adhering to the manufacturer’s operating specification to avoid premature battery degradation is highlighted, and a comparative analysis is performed with a simple tariff-driven solution, evaluating total cost, energy exchange, and peak power. Tests are carried out in a detailed model, where Power Electronics Converters (PECs) and their local controllers are considered together with the EMS.

Energy cost optimization through load shifting in a photovoltaic energy-sharing household community

Home energy management systems are essential for the optimization of resources in complex demand scheduling problems that require energy efficiency in homes. This can be achieved through the use of Renewable Electricity Sources (RES), for cleaner and more sustainable energy generation, as well as participation in Demand Response (DR) programs, for lower energy prices. The problem becomes more complex when it is considered a community of households. This paper aims to address individual and community household participation in DR programs and RES sharing while considering constraints imposed on the operation schedule of appliances, through load shifting optimization. For that, a Genetic Algorithm is proposed, implemented, and validated, which focuses on minimizing energy costs. It takes into account dynamic pricing, distributed generation, and household community energy sharing. Using real household workload data, two case studies are presented, one for the cost optimization of an individual household and another for a photovoltaic energy-sharing household community containing twenty houses. Both case studies represent five days of scheduling, where each house can have up to five appliances able to shift. The business as usual costs are 16.74 EUR and 269.99 EUR for the individual and community case studies, respectively. Results show improvements of up to 24.3% (12.67 EUR in the optimized schedule, 4.07 EUR in savings) for the individual case study and 11.8% (238.21 EUR in the optimized schedule, 31.78 EUR in savings) for the household community. In a community, households can expect cost reductions of 1.5%–26.8% when compared to individual scheduling.

Communicationless Overcurrent Relays Coordination for Active Distribution Network Considering Fault Repairing Periods

This paper presents an integrated overcurrent relays coordination approach for an Egyptian electric power distribution system. The protection scheme suits all network topologies, including adding distribution generation units (DGs) and creating new paths during fault repair periods. The optimal types, sizes, and locations of DGs are obtained using HOMER software (Homer Pro 3.10.3) and a genetic algorithm (GA). The obtained values align with minimizing energy costs and environmental pollution. The proposed approach maintains dependability and security under all configurations using a single optimum setting for each relay. The calculations consider probable operating conditions, including DGs and fault repair periods. The enhanced coordination procedure partitions the ring into four parts and divides the process into four paths. The worst condition of two cascaded overcurrent relays from the DGs’ presence viewpoint is generalized for future work. Moreover, a novel concept addresses the issue of insensitivity during fault repair periods. The performance is validated through the simulation of an Egyptian primary distribution network.

A personal touch to demand response: An occupant-centric control strategy for HVAC systems using personalized comfort models

HAVC systems account for the majority of energy use in buildings. Therefore, research efforts have been made to develop control strategies to improve energy efficiency during the day and peak time. Studies have traditionally emphasized energy optimization while treating occupant experience using temperature constraints or standard generic metrics of comfort. A well-known strategy, in this category, is the use of a penalizing term when the temperature in an environment is deviated from a pre-defined setpoint. However, in reality, individual differences lead to diverse preferences and sensitivities to indoor thermal environments. Prior studies have not systematically evaluated the impact of such differences on user experience when using advanced control strategies for demand response. Accordingly, in this study, we have proposed a novel occupant-centric control framework (PICO: Personalization-Integrated Co-Optimization) that seeks to minimize energy cost (using dynamic pricing) with a penalizing term that is informed by probabilistic personalized comfort models of the occupants. We hypothesized that such integration results in increased efficiency (during the day and peak time) and improved user experience. Through a comprehensive uncertainty quantification analysis (to account for diversity in occupants' preferences, sensitivities, and number of occupants), we evaluated this framework by comparing it against three commonly used control strategies with varying levels of emphasis on user experience. Our analysis using numerous realizations of the framework operation for different combinations of simulated occupants showed that the proposed framework can adapt to different scenarios and improve the efficiency of operations. Summarizing the energy saving and user comfort experience metrics in an energy productivity measure (that quantifies the comfort gain per unit of energy use) we demonstrated that PICO increases peak time productivity up to 18.37% across various realizations. Moreover, the framework was demonstrated to be more consistent in providing an improved user experience reflected in a considerable reduction of standard deviations for thermal comfort experience, specifically for one occupant scenarios.

Batteries or silos: Optimizing storage capacity in direct air capture plants to maximize renewable energy use

Direct air capture (DAC) of carbon dioxide is among the technologies that is forecast to play a major role in achieving the global ambition to constrain atmospheric temperature rise to below two degrees Celsius by 2100. However, DAC is an energy intensive chemical process, whose designs are currently incompatible with intermittent renewable energy (RE) sources. This research develops a model to enable the flexible operation of DAC, to maximize RE usage. A new model of the chemical process flow of a liquid solvent DAC that includes silos to store CaCO3 and CaO is developed. A linear programming optimization model that minimizes energy costs while achieving the CO2 capture targets of the DAC plant is developed. Scenario analysis establishes the storage silo size and battery storage size needed to reduce renewable energy curtailment to zero for a given RE profile. Simulations with a representative 336-hour RE profile reveal that two silos of sizes 660 tons and 370 tons would be needed to support flexible DAC plant operations and reduce RE curtailment to zero. For the same profile, a 355 MWh/65 MW battery would be required to achieve zero renewable curtailment. The results demonstrate that flexible operation of DAC is achievable, and DAC plants can adapt to variable RE without the need for battery energy storage. Furthermore, considering the scale of storage needed to minimize RE curtailment in a commercial-scale DAC plant, the results suggest that using physical storage silos could be more cost-effective than using battery energy storage.

A memetic NSGA-II for the multi-objective flexible job shop scheduling problem with real-time energy tariffs

AbstractRising costs for energy are increasingly becoming a vital factor for the production planning of manufacturing companies. Manufacturers face the challenge to react to dynamic energy prices and design energy cost efficient schedules in their production planning. In the literature, the energy cost-aware Flexible Job Shop Scheduling Problem addresses minimization of both makespan and energy costs. Recent studies provide multi-objective approaches to model the trade-off of minimizing makespan and energy costs. However, the literature is limited to coarse-grained time periods and does not consider dynamic tariffs where costs change at short intervals, so that production schedules may fall short on energy costs. We aim to close this research gap by considering frequently changing real-time energy tariffs. We propose a multi-objective memetic algorithm based on the non-dominated sorting genetic algorithm (NSGA-II) with both makespan and energy cost minimization as the objectives. We evaluate our approach by conducting computational experiments using prominent FJSP-benchmark instances from the literature, which we supplement with empiric dynamic energy prices. We show results on method performance and compare the memetic NSGA-II with the results of an exact state-of-the-art solver. To investigate the trade-off between a short makespan and low energy costs, we present solutions on the approximated Pareto front and discuss our results.

OPTIMAL ENERGY MANAGEMENT IN MICROGRIDS CONSIDERING SUPPLY DEMAND RATE AND BATTERY DISCHARGE DEPTH

Integrating solar energy with battery energy storage systems (BESS) is critical in sustainable development plans and carbon neutrality goals. Can the energy exchange between supply and demand offer hope via effective management of BESS operations? How will the depth of discharge in microgrids affect individual BESS prosumers? Motivated by such questions, this study determines the minimum energy costs and optimal energy management considering the BESS discharge depth for industrial prosumers with different PV power production to electricity demand ratios. In addition, the impact of Epv/Eload and depth of discharge on individual PV-BESS microgrid prosumers is evaluated annually from a technical, economic, and environmental perspective. Moreover, considering the negative impact of the self-consumption rate (SCR) on the low voltage distribution network (overvoltage, power loss, etc.), unfavorable depth of discharge thresholds and Epv /Eload are determined. The optimization framework is built in Python Gurobi, and Mixed Integer Linear Programming solves the complex problem. The results show that a higher Epv /Eload can reduce the cost of energy (COE) by up to 84.1% and increase the renewable fraction (RF) and electricity sales revenues by up to 61% and up to 570.25 $/yr. It also emphasizes that for Prosumer 5, with the highest Epv /Eload (176.5%), each depth of discharge is not feasible due to SCR. In contrast, a higher depth of discharge can increase CO2 reduction by up to 4.45 tons/yr and thus provide additional revenues of up to 197.41 $/yr. Evaluating BESS operations in microgrid energy management will help many stakeholders determine reliable investments and help in the planned transition to clean energy.

Soft robotics informs how an early echinoderm moved.

The transition from sessile suspension to active mobile detritus feeding in early echinoderms (c.a. 500 Mya) required sophisticated locomotion strategies. However, understanding locomotion adopted by extinct animals in the absence of trace fossils and modern analogues is extremely challenging. Here, we develop a biomimetic soft robot testbed with accompanying computational simulation to understand fundamental principles of locomotion in one of the most enigmatic mobile groups of early stalked echinoderms-pleurocystitids. We show that these Paleozoic echinoderms were likely able to move over the sea bottom by means of a muscular stem that pushed the animal forward (anteriorly). We also demonstrate that wide, sweeping gaits could have been the most effective for these echinoderms and that increasing stem length might have significantly increased velocity with minimal additional energy cost. The overall approach followed here, which we call "Paleobionics," is a nascent but rapidly developing research agenda in which robots are designed based on extinct organisms to generate insights in engineering and evolution.

Smart Management of Energy Storage in Microgrid: Adapting the Control Algorithm to Specific Industrial Facility Conditions

The article introduces a method for optimizing energy storage system scheduling in industrial microgrids. It employs a PSO-based heuristic algorithm using daily generation and load forecasts. The objective is economic optimization, minimizing energy costs, and maximizing profits. Market energy prices and distributor tariffs are the base of the objective function. An algorithm maintains the plan by controlling storage power based on real-time microgrid measurements, aligning with the intended power exchange curve. Due to PSO’s ability to perform multidimensional optimization, it is possible to find the global optimum of the objective function. To validate the practical applicability of this approach, it is exemplified through its implementation within a real-world industrial microgrid setting. The presented results indicate the method’s effectiveness but also show its weaknesses. For the two considered cases, a decrease in operating costs of 6.7% and 10.8% was achieved, respectively. On the other hand, the best results are obtained for shorter forecasts, which is why the algorithm, despite long planning periods, revises the ESS operation plan whenever there are significant deviations between the forecast and the actual power.

A techno‐economic assessment and optimization of Dumat Al‐Jandal wind farm in Kingdom of Saudi Arabia

AbstractOne major criterion in the selection of wind farm location is the cost of energy (COE). COE is the cost of producing 1 kWh electric energy on an annual basis. Mathematical model of COE includes site‐specific constants (such as reference height, mean wind speed, shape factors, wind shear coefficient, average temperature, and turbine altitude) and wind turbine parameters (such as maximum power coefficient, total loss of energy, cut‐in/cut‐off wind speed, rated wind speed, rated power, and the fix charge rate). In this work, we evaluate the COE of an onshore wind farm located at Dumat Al‐Jandal (Saudi Arabia) according to the hub height and rotor size. The 99 Vestas turbines can be mounted at a hub height ranging from 105 to 166 m with available rotor diameters of 105, 112, 117, 126, 136, 150, 155, or 163 m. Particle swarm optimization with a normal distribution is used to optimize the COE. Results show that COE is varying around the average value of $0.029335/kWh by ±$0.00021/kWh. The minimum COE was achieved with a rotor diameter of 150 m at hub height of 105 m. COE increases with the increase of hub height. At 105 m‐hub height, COE is almost the same, with a variation of 0.03% (It ranges between $0.029125/kWh and $0.029133/kWh). COE is more sensitive to rotor size than hub height. This investigation revealed that the COE estimation is in a range of 39%–48% greater than that announced COE by the developing project consortium.

Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

We aim to preserve a large amount of data generated inside base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP: d ata p reservation p roblem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit. In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.

Data-driven control of airborne infection risk and energy use in buildings

The global devastation of the COVID-19 pandemic has led to calls for a revolution in heating, ventilation, and air conditioning (HVAC) systems to improve indoor air quality (IAQ), due to the dominant role of airborne transmission in disease spread. While simple guidelines have recently been suggested to improve IAQ mainly by increasing ventilation and filtration, this goal must be achieved in an energy-efficient and economical manner and include all air cleaning mechanisms. Here, we develop a simple protocol to directly, quantitatively, and optimally control transmission risk while minimizing energy cost. We collect a large dataset of HVAC and IAQ measurements in buildings and show how models of infectious aerosol dynamics and HVAC operation can be combined with sensor data to predict transmission risk and energy consumption. Using this data, we also verify that a simple safety guideline is able to limit transmission risk in full data-driven simulations and thus may be used to guide public health policy. Our results provide a comprehensive framework for quantitative control of transmission risk using all available air cleaning mechanisms in an indoor space while minimizing energy costs to aid in the design and automated operation of healthy, energy-efficient buildings.

Techno-economic modelling for energy cost minimisation of a university campus to support electric vehicle charging with photovoltaic capacity optimisation

Workplace charging of Electric Vehicles (EV) is a promising approach for transport decarbonization while addressing issues emerging from renewable energy growth. For workplaces, the decision to invest in EV charging infrastructure depends on the overall cost of providing this service. This study proposes a novel modelling approach to minimize the net annual energy cost of a university campus providing EV charging service using optimum capacity solar photovoltaic (PV) systems. The research includes an innovative approach to determine the campus EV charging demand and a novel net annual energy cost minimisation method combining PV size optimisation and EV charging control. A comprehensive analysis is presented to illustrate the influence of EV penetration, charging strategy, charging fees, charger cost and PV generation cost on the campus net annual energy cost. The influence of these parameters are also analysed on the optimal PV capacity, power and energy demand, and PV self-utilisation. Results show that, by using the proposed method, for 25 % EV penetration, the campus's peak demand is reduced by around 12 % and net annual energy cost is reduced by up to 9.2 % while providing free EV charging. The net annual energy cost reduction increases to over 20 % for 100 % EV penetration.

Forecasting flexibility of charging of electric vehicles: Tree and cluster-based methods

Scheduling electric vehicle charging sessions allows to aggregate flexibility in order to minimize energy costs and reduce congestion in the electricity grid. Existing research shows that user input for energy and parking duration does not serve as a reliable prediction. The study presents an evaluation of the forecast error and computational performance of the different models. Two main methods are investigated: tree-based (gradient boosted trees LightGBM) and cluster-based (Gaussian mixture model). We also present a novel dynamic cluster-based method, the Similar Sessions method, which employs the similarity between charging sessions based on numerical variables. The results highlight the importance of selecting the forecast model influenced by the availability of training data. The effect of user registration on the accuracy of the forecast is investigated. The tests are run using ACN-Data dataset of Electric Vehicle charging sessions in California, United States. While underperforming on a small dataset with a short look-back period, tree-based methods show superiority while the charging data are accumulating. The Similar Sessions method shows superior accuracy under various data availability conditions. The proposed method requires no prior training, but has slower computational performance in deployment.

Energy Cost Minimization in Wireless Rechargeable Sensor Networks

Mobile chargers (MCs) are usually dispatched to deliver energy to sensors in wireless rechargeable sensor networks (WRSNs) due to its flexibility and easy maintenance. This paper concerns the fundamental issue of charging path DEsign with the Minimized energy cOst (DEMO), i.e., given a set of rechargeable sensors, we appropriately design the MC’s charging path to minimize the energy cost which is due to the wireless charging and the MC’s movement, such that the different charging demand of each sensor is satisfied. Solving DEMO is NP-hard and involves handling the tradeoff between the charging efficiency and the moving cost. To address DEMO, we first investigate how to identify a single charging position where the MC could stay to charge a set of sensors distributed within a small area with the maximized charging efficiency. Then, based on the result obtained in the case of optimizing a single charging position, we develop a computational geometry-based algorithm to deploy multiple charging positions within the whole network, by considering the fixed and finite charging range of the MC. We prove that the designed algorithm has the approximation ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O\!\left(\ln\!N\right)$</tex-math> </inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$</tex-math> </inline-formula> is the number of sensors. Then we construct the charging path by calculating the shortest Hamiltonian cycle passing through all the deployed charging positions within the network. In addition, we investigate the impact of the network topology as well as the distribution of charging demands among sensors on the MC’s energy cost during a charging tour. Extensive evaluations validate the superiority of our path design in terms of the MC’s energy cost minimization, compared with existing main algorithms.

Operation of the stand-alone electrical system based on optimal energy management

This paper focuses on optimizing the energy of a stand-alone energy grid by incorporating energy storages and demand side participation. The increasing popularity of energy storage systems and demand side participation is mainly attributed to their advantages, such as the ease of distribution in electrical networks and operational flexibility during critical periods. The main objective functions of the model involve optimizing the stand-alone electrical grid by maximizing efficiency and minimizing energy costs. The efficiency is modelled by energy not supplied. The costs of energy consumption are represented by the fuel costs consumed by resources. To encourage consumers to reduce their demand during peak times, an incentive approach is suggested for their participation. Additionally, a hydrogen storage system is operated in the stand-alone energy grid to enhance the primary objectives. The particle swarm optimization (PSO) algorithm is utilized to optimize energy usage and solve objective functions. Numerical simulations are conducted in two case studies to validate and demonstrate the effectiveness of energy optimization when incorporating storage system and consumers. The case studies based on non-participation and participation of the storage system and consumers in the energy management are implemented. With participation of the storage system and consumers, efficiency and energy costs are improved by 3% and 29.8% than non-participation of them.

Real-time rolling-horizon energy management of public laundries: A case study in HSB living lab

Energy Management Systems (EMSs) play a vital role in managing energy consumption for both utilities and consumers. By using EMSs, utilities can influence on energy usage and ensure a more reliable and efficient grid operation, while consumers can make informed decisions about their energy consumption, leading to significant cost savings and reduced environmental impact. In this paper, a real-time rolling-horizon model is developed for managing energy consumption in public laundries aiming at minimizing energy costs, peak demand, and CO2 emission under the traditional Energy-Based Tariff (EBT) and the Power-Based Tariff (PBT). The developed model can not only reduce energy costs, peak demand, and CO2 emission by optimal task scheduling for washing machines and tumble dryers but also ensure users' preferences for a comfortable lifestyle. To demonstrate the effectiveness of the proposed EMS, several simulations were performed under different scenarios using real data and by a realistic case study in HSB living lab demonstration site. The simulation results reveal that implementing the proposed EMS can significantly decrease energy costs and peak demand in public laundries by 13.59% and 39.40%, respectively, when using the PBT tariff. However, the reduction in energy costs and peak demand is negligible when using the EBT tariff. Likewise, the results indicate that using the EMS and changing tariffs have a minimal impact on CO2 emissions reduction.

Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning

Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper, we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices, renewable energy production, and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this, we propose a multi-agent deep deterministic policy gradient approach, which is a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real time. The proposed approach is model-free and does not require explicit knowledge and rigorous mathematical models of the smart energy network environment. Simulation results based on real-world data show that (i) integration and optimised operation of the hybrid energy storage system and energy demand reduce carbon emissions by 78.69%, improve cost savings by 23.5%, and improve renewable energy utilisation by over 13.2% compared to other baseline models; and (ii) the proposed algorithm outperforms the state-of-the-art self-learning algorithms like the deep-Q network.

A Techno-Economic Comparison of a Stand-alone Hybrid System for a Household: A Case Study for the Royal Commission in Saudi Arabia

The trend is now to use renewable energy sources to improve energy efficiency in the building sector and preserve the environment. The use of sources as renewable off-grid applications for driven applications in home sector (micro-grids) is very interesting nowadays. The economic factor and the cost of establishing these grids is the main obstacle to these investments. One of the key solutions to reduce the cost and increase the reliability of these grids is how to optimally determine the ratings and combination of some renewable sources based on the location of the house. This paper presents an effective techno-economical design of a stand-alone hybrid system. The system contains a photovoltaic (PV), wind turbine, battery, and diesel generator. This design is performed through an extensive examination of the techno-economical design of a standalone system to provide the desired energy load for a typical household building for both Albaha and Buraydah cities in Saudi Arabia. These two cities have different geographical locations as Albaha is located at latitude 20.0217 degrees north while Buraydah is located at 26.3592 degrees north. These two cities were selected due to the difference in their geographical locations to study the effect of the weather on the building performance in terms of cost of energy, energy penetration, and load met. To enhance the performance of the proposed hybrid system, some constraints were selected, such as Net Present Cost (NPC), and Total Annual Cost (TAC). Results demonstrate that stand-alone hybrid renewable energy systems can be applied to improve energy efficiency and economics of the existing buildings. Integration of PV system, wind turbine, battery, and diesel generator in the best configuration can provide a minimum cost of energy of about $0.596/kWh and $0.603/kWh for Albaha and Buraydah cities, respectively.