Cost-effective Energy Management Research Articles

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across four distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs superior to the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

Decentralized energy management regime for prosumer community Microgrids using flexible tube model predictive control

Achieving reliable, cost-effective, and low-carbon energy management for community microgrids (CMs) under suitable computational complexity is challenging due to uncertainties in renewable generation and load demand. To address this challenge, this paper proposes a flexible tube model predictive control-based energy management regime (EMR) for prosumer CMs, enabling robustness against system uncertainties in the energy management strategy with fewer sacrifices in economic performance and computational efficiency. The proposed EMR adopts a multistage receding horizon optimization structure, which allows the prosumers to dynamically adjust their energy scheduling according to the updated energy price and forecast information. Further, a decentralized algorithm with a conditional communication strategy is developed, highlighting less communication burden and private information preservation. Case studies on a typical prosumer CM demonstrate the effectiveness of the proposed method in confronting high system uncertainties. Notably, the proposed method ensures that CMs operate with high reliability (having a maximum energy supply shortage probability of just 0.42 %), in a low-carbon and economical manner (the local accommodation rate of renewable energy generation with low costs reaches 98.83 %), even in scenarios where disturbances occur simultaneously on both the energy supply and demand sides.

Robust economic-resilient operational planning of distribution networks considering multi-microgrids black-start service

The increasing growth of natural disasters and cyber-physical attacks has made the development of resilience enhancement strategies for distribution networks a significant challenge for operators. Hence, in this paper, a black-start strategy based on multi-microgrids capability is developed as a two-layer preventive-corrective measure within a cost-effective energy management framework. The preventive layer includes regulating bilateral contracts between the active distribution network operator and the multi-microgrids owner pre-event. The corrective layer includes islanding microgrids during the event, calling the responsive loads, and economic-resilient operational planning of the active distribution network post-event. In this regard, a hybrid robust multi-objective optimization approach is applied to model the proposed black-start strategy as a risk-averse portfolio. The effectiveness of the developed model is tested on the modified IEEE 33-bus distribution network. Results such as an 83.38 % load recovery of the active distribution network, a 65.78 % reduction in distribution system operator costs, and a 131.28 % increase in multi-microgrid owner profit clearly illustrate the enhanced resilience of the active distribution network within an economically robust framework, which indicates the effectiveness of the proposed strategy.

An integrated system of energy generation, storages, and appliances consumption based on machine learning techniques and internet of things

In the swiftly evolving micro-grid landscape, this paper explores the pivotal role of residential consumers and their active engagement. The primary focus is on revolutionizing customer management through the integration of smart homes, with an emphasis on facilitating demand response and bolstering operational efficiency. Also, the innovative Power Generation, Electricity Storages, and Energy Consumption in Smart Homes (PG–ES–ECSH) approach is introduced, meticulously designed for next-generation technologies, and delve into an smart home load control system. This system, employing Extreme Machine Learning (EML) and an imprecise reasoning framework, aims to enhance computational efficiency in managing diverse electrical energy sources and small-scale power generation appliances. The decision-making process of the framework incorporates real-time monitoring of environmental data, power generation, and battery storage. Our research significantly contributes to the evolution of smart homes and micro-grids by embracing technologies crucial for the future, such as machine learning and the Internet of Things (IoT). In this paper, the application of IoT and EML are used to enhance the connection between users in the microgrid and the learning rate of the proposed system, respectively. Emphasizing the burgeoning significance of energy management in smart homes, our study underscores the profound impact of energy resources on overall quality of life. With the increasing prevalence of smart homes, there is a growing demand for efficient energy utilization and management. Noteworthy is the PG–ES–ECSH method's impressive accuracy ratio of 96 % and a precision rate of 92 %. These findings highlight the imperative need for cost-effective energy management solutions, particularly in the face of escalating electricity consumption and the integration of novel power resources.

Optimum exploitation of multiple energy system using IGDT approach and risk aversion strategy and considering compressed air storage with solar energy

The concept of an energy hub within a microgrid has garnered significant attention from investors due to its potential for efficiently managing diverse energy sources and accurately forecasting energy prices. This study presents a model for an energy hub that facilitates the optimal operation of a microgrid with multiple energy carrier infrastructures under future-day scenarios. The primary objective of the optimization process is to minimize operating costs, environmental impacts, and technical limitations. The proposed energy hub model integrates both distributable generation, such as a Combined Cooling, Heating, and Power (CCHP) unit, and non-distributable generation sources, including wind turbines and photovoltaic systems. Additionally, it incorporates various energy storage components, namely Ice Storage Conditioner (ISC), Thermal Energy Storage (TES) systems, and Electric Vehicles (EVs). Furthermore, the study investigates the impact of a novel energy storage system called Solar Powered Compressed Air Energy Storage (SPCAES) on the operational and environmental performance of the energy hub. To address uncertainties stemming from predicted and actual variables, a two-level stochastic optimization problem is formulated based on the Information Gap Decision Theory (IGDT) with a Risk Aversion (RA) strategy. The overarching aim is to mitigate the risks associated with the information gap confronted by microgrid operators. Subsequently, the two-level stochastic optimization problem is transformed into a single-level problem using the Karush-Kuhn-Tucker method. The proposed framework employs the RA-IGDT method and utilizes the Elephant Herd Optimization (EHO) algorithm to effectively solve the optimization problem. By applying this proposed framework to a typical energy hub, this research demonstrates the effectiveness of energy storage systems in reducing operating costs and greenhouse gas emissions during daily energy management. The study highlights the potential of energy hubs and storage systems for cost-effective and eco-friendly microgrid energy management. Scenario 4, featuring the proposed energy hub with SPCAES and ISC storage, demonstrates a 15 % reduction in operating costs and a notable 20 % increase in peak-hour electrical power for sale, underscoring its economic and operational benefits.

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.

A Novel Hybrid Imperialist Competitive Algorithm–Particle Swarm Optimization Metaheuristic Optimization Algorithm for Cost-Effective Energy Management in Multi-Source Residential Microgrids

The integration of renewable sources and energy storage in residential microgrids offers energy efficiency and emission reduction potential. Effective energy management is vital for optimizing resources and lowering costs. In this paper, we propose a novel approach, combining the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) as ICA-PSO to enhance energy management. The proposed energy management system operates in an offline mode, anticipating data for the upcoming 24 h, including consumption predictions, tariff rates, and meteorological data. This anticipatory approach facilitates optimal power distribution among the various connected sources within the microgrid. The performance of the proposed hybrid ICA-PSO algorithm is evaluated by comparing it with three selected benchmark algorithms, namely the genetic algorithm (GA), ICA, and PSO. This comparison aims to assess the effectiveness of the ICA-PSO algorithm in optimizing energy management in multi-source residential microgrids. The simulation results, obtained using Matlab 2023a, provide clear evidence of the effectiveness of the hybrid ICA-PSO algorithm in achieving optimal power flows and delivering substantial cost savings. The hybrid algorithm outperforms the benchmark algorithms with cost reductions of 4.47%, 14.93%, and 26% compared to ICA, PSO, and GA, respectively. Furthermore, it achieves a remarkable participation rate of 50.6% for renewable resources in the energy mix, surpassing the participation levels of the ICA (42.88%), PSO (40.51%), and GA (38.95%). This research contributes to the advancement of power flow management techniques in the context of multi-source residential microgrids, paving the way for further research and development in this field.

Energy Management Strategies of Grid-Connected Microgrids under Different Reliability Conditions

The demand for a reliable, cheap and environmentally friendly source of energy makes the integration of renewable energy into power networks a global challenge. Furthermore, reliability, as one of the core elements of efficient and cost-effective energy management options, is still among the dominant factors/techniques that receive more attention for realistic penetrations of renewable energy into the electricity grid. This paper proposes an efficient way of energy management for a grid-connected microgrid. The grid-connected microgrid used in the analysis consists of solar photovoltaic (P.V.) and battery. In this microgrid configuration, oftentimes, the output power might not be equal to the system demand; in this regard, it is expected that the mismatch between these output powers is not zero. However, to reduce the mismatch between demand and supply to be close to zero, this paper proposes strategies of increasing the rated power of solar, battery and grid separately and combining them with a view of finding the cheapest option among these strategies. The results have shown that the cost increment for different options is USD 280.792, 84.48 and 48.204 for storage, P.V. and grid, respectively. These have shown that the storage option is the most expansive option for improving P.V. grid-connected microgrids. This is followed immediately by the P.V. option, which is weather dependent. On the other hand, the grid option is the cheapest option for system reliability improvement. This paper is expected to be useful to both new researchers and experts who are working in energy management with an emphasis on the reliability aspect.

Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

The large-scale integration of distributed energy resources into the energy industry enables the fast transition to a decarbonized future but raises some potential challenges of insecure and unreliable operations. Multi-energy Microgrids (MEMGs), as localized small multi-energy systems, can effectively integrate a variety of energy components with multiple energy sectors, which have been recently recognized as a valid solution to improve the operational security and reliability. As a result, a massive amount of research has been conducted to investigate MEMG energy management problems, including both model-based optimization and model-free learning approaches. Compared to optimization approaches, reinforcement learning is being widely deployed in MEMG energy management problems owing to its ability to handle highly dynamic and stochastic processes without knowing any system knowledge. However, it is still difficult for conventional model-free reinforcement learning methods to capture the physical constraints of the MEMG model, which may therefore destroy its secure operation. To address this research challenge, this paper proposes a novel safe reinforcement learning method by learning a dynamic security assessment rule to abstract a physical-informed safety layer on top of the conventional model-free reinforcement learning energy management policy, which can respect all the physical constraints through mathematically solving an action correction formulation. In this setting, the secure energy management of the MEMG can be guaranteed for both training and test procedures. Extensive case studies based on two integrated systems (i.e., a small 6-bus power and 7-node gas network, and a large 33-bus power and 20-node gas network) are carried out to verify the superior performance of the proposed physical-informed reinforcement learning method in achieving a cost-effective MEMG energy management performance while respecting all the physical constraints, compared to conventional reinforcement learning and optimization approaches.

Efficient Energy Management and Reliability Assessment by Optimal Placement of Renewable Energy Sources with Pump Storage Plant

Deteriorating environment conditions due to global warming and depleting fossil fuels, has forced mankind to shift towards renewable energy sources (RES). The integration of RES in existing power system is challenging due to its intermittent nature. This study formulates novel sensitivity index i.e. nodal price of energy index (NPEI) to assist the location of sensitive buses for optimal placement of RES. Firstly, the proposed approach subjects power system to (N-1) contingency including both loss of generator and line outage. The NPEI is computed for every bus in various contingency conditions. The most severe contingency is identified based on highest value of sensitivity index in peak load period. The buses with highest values of NPEI is the optimal location for RES placement in the most critical contingency scenario. Secondly, the proposed approach focuses on efficient and cost-effective energy management by optimally scheduling disparate energy generation sources. The system incorporates pumped storage plant (PSP) based energy storage devices to effectively utilize wind and solar energy. The objective of this study is to minimize the total generation cost which includes the generation cost associated with conventional generators, its emission cost and valve-point loading effect, generation cost of RES, and the cost associated with energy supplied by PSP. The partial shading of PV modules is also implemented and its impact on system parameters are observed. The penetration of RES and energy storage effects system reliability hence, the proposed approach computes reliability indices expected energy not supplied (EENS) and loss of load expected (LOLE) for both presence and absence of RES and PSP. Finally, the proposed approach results in optimal placement of RES and optimal energy scheduling of disparate energy sources along with analyzing its effect on system reliability. The study is performed on IEEE 30 bus system.

Adaptation and optimization of a photovoltaic system for a country house

This chapter provides the rationale for monitoring photovoltaic (photovoltaic) systems, its purpose, the need for proper measurement, and the frequency required to obtain meaningful results. The need for system monitoring falls into three groups: user feedback, performance review, and system evaluation. Each group is briefly summarized; other related topics include calibration, data storage, and data transfer. The text covers various monitoring modes such as performance testing, system evaluation, data collection and storage, as well as data analysis and reporting. The evaluation standards given by the International Electrotechnical Commission (IEC) are noted in the Data Analysis and Reporting section. The figures are provided to illustrate several types of displays that are an integral part of monitoring. Optimized modes of operation of the power part of a household photovoltaic system consisting of photovoltaic panels, an MRRT controller, an inverter and an electric energy storage unit, with Autonomous and network connection of the load. To build an optimal and cost-effective energy management strategy, it is necessary to take into account the energy profile of the resident, the characteristics of electricity production based on photovoltaic cells, and the electricity tariff for utilities. A photovoltaic system can be connected to the grid in order to receive energy from the grid or enter it into the network to eliminate the discrepancy between the generated, consumed and stored energy.

FMI real-time co-simulation-based machine deep learning control of HVAC systems in smart buildings: Digital-twins technology

As heating, ventilation, and air conditioning (HVAC) systems have become one of the most contributing systems in energy consumption in the world, the control of these large-scale systems remains a challenging duty due to the decoupling effects of control variables. Accordingly, the penetration of these types of systems in all-smart buildings has increased in recent years. Furthermore, the application of digital twin as a fast-growing concept is being developed. In HVAC systems, independent and accurate control of temperature and humidity of the indoor air has been playing an undeniable role in reducing energy consumption. In this paper, to have cost-effective energy management in a single-zone HVAC system, a new reliable digital twin proximal policy optimization (PPO)–based model-independent nonsingular terminal sliding-mode control (MINTSMC) methodology has been proposed. Moreover, due to the nonlinear characteristics of HVAC systems, MINTSMC tends to handle the un-modeled system dynamics and disturbances. For regulating parameters of proposed control, an efficient PPO algorithm has been developed due to its actor-critic-based reinforcement learning. Extensive examinations and comparative analyses with particle swarm optimization designed sliding-mode control and proportional–integral–derivative controller have been made using digital twin of the proposed controller to show the importance, accuracy, and application of this method in the comfort and energy management achievement of HVAC control systems. A digital signal processor computing device has been utilized for implementation by utilizing hardware-in-loop (HIL) in the concept of the digital twin. To determine the interface between established models, software-in-loop, and HIL, the Functional Mock-up Interface has been utilized. The outcomes revealed a superior performance of suggested digital twin-based controller than the compared control methodologies in the compensation of unknown uncertainties, fast-tracking, and smooth response.

Modeling and analysis of cost-effective energy management for integrated microgrids

A microgrid concept is an innovative approach for integrating hybrid and renewable energy sources into the utility grid. The uncertainties because of the intermittent nature of renewable energy resources, the load, and market price are significant challenges. In the traditional heuristic method, data is forecast but not known perfectly. Improving energy storage systems and energy management systems (EMS) development using optimization-based methods is a possible solution to improve the performance of microgrid operations. The EMS is an essential part of the distributed energy resources in the microgrid system, especially when power generation, transmission, distribution, utilization, and variable pricing are involved. This optimization process developed in this paper uses forecasted costs and loading conditions to store or sell the energy from an integrated grid battery system. Two approaches are introduced in this research work: the heuristic method using state flow (chart flow) and the optimization method based on linear programming (LP), which minimizes operation costs (savings of around 19% cost) subject to operational constraints. The LP optimization saves roughly 3.44–5.01% of excess grid energy. Several plausible outcomes of this research study simplify the comprehensive, integrated microgrid simulation for EMS optimization algorithm validation. The suggested integrated microgrid management system might be a testbed for smart grid technology research. • This paper uses linear programming optimization methods to develop a microgrid energy management system (EMS). • A constrained linear program (LP) optimization minimizes the total cost of electricity consumption. • The case studies are presented to show the performance of the proposed optimization and compare it with the heuristic approach. • The method predicts pricing and loading conditions and optimally stores/sells energy from a grid-scale battery system. • The cost of electricity consumption in the optimization method is around 19% less than in the heuristic method.

Optimal heuristic economic management strategy for microgrids based PEM fuel cells

This paper provides a cost-effective energy management method for the microgrid's standalone and on-grid operational modes. The considered microgrid based on Green-to-Green systems such as a photovoltaic array (PV), wind turbine (WT) as renewable generators, hydrogen fuel cell (FC) system, microturbine (MT), and battery storage system. The suggested technique is based on the marine predator algorithm (MPA) that and created for a scheduling horizon of one day. The main objective of this paper is to reduce operating costs while satisfying the demand power. To approve the performance of the suggested management strategy, the simulation results are compared with other recent optimization algorithms. These algorithms include particle swarm optimization (PSO), salp swarm algorithm (SSA), and coot optimization algorithm (COOT). To approve the performance of the proposed EMS, the system will be evaluated for three cases: free renewable generation, restricted generation mode, and unlimited main grid power mode. For the first case, the proposed EMS reduces the operating costs by 0.8732%. For case 2, the economic benefits have been raised by 1.0815% and by 0% for the last case because of the reduced problem complexity.

A QR code based framework for auto-configuration of IoT sensor networks in buildings

Worldwide buildings are responsible for about 40% of the overall consumption and contribute to an average of 30% percent of the global carbon emissions. Nevertheless, most current buildings lack efficient energy management systems because such solutions are very expensive, especially when necessary instrumentation needs to be installed after the building’s construction. As an alternative, we purpose the use of IoT sensor networks to retrofit existing medium and large-sized buildings to provide energy management capabilities in a cost-effective way. An IoT network auto-configuration platform for building energy management was developed. In order to efficiently manage metadata related to location and devices, a database using dynamic QR codes was created. Furthermore, we discuss the potential and shortcomings of different sensor-gateway pairing strategies that are applicable to an auto-configuring system. Lastly, we share our implementation of these concepts and demonstrate their use in a medium-sized building case study. The results show a trade-off between optimal configuration and total configuration time with a focus on the quality of the communication signal strength. The proposal provided the necessary automation for a cost-effective energy management system that can be deployed in both new constructions and existing buildings.

General Aspects, Islanding Detection, and Energy Management in Microgrids: A Review

Distributed generators (DGs) have emerged as an advanced technology for satisfying growing energy demands and significantly mitigating the pollution caused by emissions. Microgrids (MGs) are attractive energy systems because they offer the reliable integration of DGs into the utility grid. An MG-based approach uses a self-sustained system that can operate in a grid-tied mode under normal conditions, as well as in an islanded mode when grid disturbance occurs. Islanding detection is essential; islanding may injure utility operators and disturb electricity generation and supply because of unsynchronized re-closure. In MGs, an energy management system (EMS) is essential for the optimal use of DGs in intelligent, sustainable, reliable, and integrated ways. In this comprehensive review, the classification of different operating modes of MGs, islanding detection techniques (IDTs), and EMSs are presented and discussed. This review shows that the existing IDTs and EMSs can be used when operating MGs. However, further development of IDTs and EMSs is still required to achieve more reliable operation and cost-effective energy management of MGs in the future. This review also highlights various MG challenges and recommendations for the operation of MGs, which will enhance the cost, efficiency, and reliability of MG operation for next-generation smart grid applications.

Multiclass Appliance Scheduling for Cost-effective Energy Management with Constraint and User Preferences

For decades, the electrical power grid worldwide has transformed from traditional to the smart power grid, focusing on its transparency to both utility and consumer. The energy management systems play a substantial part in demand response within the smart power grid umbrella, enabling demand-side management at the residential level. These systems generate the consumption profile of appliances and reduce the burden on end-user in scheduling appliances operations. With these consumption profiles of past usage, there is a possibility to generate a time window containing user preferable time slots for appliance operation for the next day. Using this time window, one can generate a cost-effective schedule-pattern autonomously. In this regard, this article proposes a home energy-demand management scheme consisting of a time window generator and a schedule-pattern generator to generate a cost-effectively comfortable schedule-pattern with demand threshold constraint. Multi-class appliances home enabled with a net-meter demonstrate the proposed approach's effectiveness. The simulation results showcase that the proposed approach helps the user to save electricity bills with constraint preserving comfort.

Priority-based low voltage DC microgrid system for rural electrification

This paper discusses the current state of DC distribution system, how it can be beneficial to isolate solar-based Micro-Grid (MG) system in a rural area, and how the priority-based approach can help the MGs to operate in cost-effective and reliable ways. DC distribution system holds many advantages over AC systems in such area, where the load demand is not so high and the electricity supply through grid extension is not feasible. Direct load control-based approach can ensure a continuous supply of power to the loads with various priorities. In this paper, an experimental analysis of the DC MGs has been presented with the help of priority based algorithms. A cost-effective SCADA system is developed to interface with the remaining parts of the system, so that the MGs can be monitor and control to get the desired performance. The main objective of this study is to identify a cost-effective and practical energy management system for the rural area, where expensive and advanced technology is not suitable. From this study, it is concluded that the proposed combination of algorithm and SCADA system increase the system’s reliability and power consistency significantly.

Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships

Hybrid fuel cell and battery propulsion systems have the potential to offer improved emission performance for coastal ships with access to H2 replenishment and battery charging infrastructures in ports. However, such systems could be constrained by high power source degradation and energy costs. Cost-effective energy management strategies are essential for such hybrid systems to mitigate the high costs. This article presents a Double Q reinforcement learning based energy management system for such systems to achieve near-optimal average voyage cost. The Double Q agent is trained using stochastic power profiles collected from continuous monitoring of a passenger ferry, using a plug-in hybrid fuel cell and battery propulsion system model. The energy management strategies generated by the agent were validated using another test dataset collected over a different period. The proposed methodology provides a novel approach to optimal use hybrid fuel cell and battery propulsion systems for ships. The results show that without prior knowledge of future power demands, the strategies can achieve near-optimal cost performance (96.9%) compared to those derived from using dynamic programming with the equivalent state space resolution.

A Numerical Fitting-Based Compact Model: An Effective Way to Extract Solar Cell Parameters

We have developed an electrical circuit-based compact numerical fitting model to determine industry-related physical parameters of solar cells utilizing only 3–8 current–voltage coordinate points without any specific selection of an experimental coordinate axis. The proposed compact numerical fitting model was effectively tested to determine the peak power point, fill factor and efficiencies for organic and inorganic solar cells, as well as for solar panels. This research facilitates cost-effective energy management of solar modules and farms.