Increasing Electricity Demand Research Articles

Fuzzy Logic-Based Dynamic Thermal Rating Assessment for Adaptive Grid Operations

Abstract: In recent times, there has been a notable increase in electricity demand, which is expected to continue rising. This uptick in demand has led to transmission lines operating at higher capacities, exposing them to greater thermal and mechanical stress and affecting the reliability of the transmission network. This paper presents an in-depth analysis of the static thermal rating (STR) and dynamic thermal rating (DTR) of power lines across various climatic conditions. The study investigates the thermal behavior of power lines during both summer and winter seasons over a 24-hour period. Through computational simulations and empirical data collection, the study determines the STR and DTR profiles to evaluate the real-time capacity of power lines under changing environmental conditions. Additionally, the research introduces a novel approach using fuzzy logic to model the dynamic thermal rating (FDTR) based on the IEEE 738 standard, particularly in regions with diverse climatic patterns. By integrating fuzzy logic with meteorological data, the FDTR model improves accuracy in predicting the thermal performance of power lines, considering factors like ambient temperature, wind speed, and solar radiation. This methodology aims to offer utilities and grid operators a reliable tool for optimizing power transmission capacity, reducing the risk of overheating, and ensuring grid reliability in areas with complex and fluctuating weather conditions. The study contributes to the advancement of smart grid technology and facilitates efficient energy management in diverse climate zones.

A transfer learning-based hybrid model with LightGBM for smart grid short-term energy load prediction

Efficient energy management is crucial given 2024's 4.1% worldwide electricity demand increase. This urgency emphasizes the necessity for various, sustainable energy sources in distribution grids. Short-term load prediction approaches using probabilistic power generation and energy storage are crucial for energy usage prediction. Urban energy planners use simulation, probability optimization and modelling to create sustainable energy systems. This study offers a novel hybrid model for smart grids: short-term energy load prediction using transfer learning (TL) and optimized lightGBM (OLGBM). Our two-phase solution tackles Short-term Load Forecasting complexities. First, aberrant supplements and quick deviation selection eliminate missing values and identify key features during data pre-processing. Second, TL-OLGBM learns dynamic time scales and complex data patterns with Bayesian optimization of hyperparameters to improve forecasting accuracy. Additionally, our architecture easily combines the newest Smart and Green Technology, enabling energy system innovation. Comparative performance research shows that our technique outperforms similar models in mean absolute percentage error, accuracy and root mean square error. This hybrid model is a reliable short-term energy load forecast solution that fits the dynamic terrain of smart and green technology integration in modern energy systems.

Intelligent Composite Compensation Under Load Variation in Power Transmission System.

The power transmission system is in a stressed condition due to the increasing electricity demand. This stress, exacerbated by the deregulated power system environment, necessitates an urgent additional supply to maintain system adequacy. One important initiative to alleviate the transmission burden which meets the load demand can be rectified by composite compensation scheme. This paper showcases intelligent composite compensation under varying loads, emphasizing loss minimization. The approach integrates a loss control scheme involving Distributed Generation (DG) and Optimal Reactive Power Dispatch (ORPD) with multi-DG installation termed composite compensation, employing a novel optimization technique called Integrated Cloning Accelerated Mutation Evolutionary Programming (ICAMEP). The study identifies optimal locations and sizes for composite compensation in the power transmission system, demonstrating its superiority over traditional optimization techniques namely the evolutionary programming (EP) and artificial immune systems (AIS). Results are demonstrated for four cases involving single DG, 3 DGs and composite compensation validated on IEEE 30-Bus Reliability Test System (RTS). ICAMEP is superior over EP and AIS in achieving the highest loss reduction.

The role of BRICS countries in the optimal low-carbon transition path for sustainable development

With the development of industrialization, greenhouse gas emissions caused by human activities, especially carbon dioxide emissions, continue to increase. The BRICS countries (Brazil, Russia, India, China, and South Africa) account for a significant share of global carbon emissions. This study focuses on the BRICS countries as the subject of inquiry, employing carbon emission-related data spanning the temporal range from 2000 to 2020. This study systematically assesses the interrelationships among variables using the Pearson correlation analysis and Granger causality test. Variables exhibiting pronounced correlation and causality, as discerned from the analytical outcomes, are subsequently identified and selected for further examination. Use the GCAM (Global Change Assessment Model) to simulate the Rcp-ref scenario (baseline scenario) and the Rcp2.6 scenario (assuming that the temperature will not rise by 2 degrees Celsius), and compare the optimal paths of key variables in the two scenarios, and draw the following conclusions. (1) By the end of this century, the total carbon emissions of the BRICS countries under Rcp2.6 scenario will be 6139MTC less than those under Rcp-ref scenario. (2) Compared with the Rcp-ref scenario, in the Rcp2.6 scenario, the proportion of fossil fuels such as oil, natural gas, and coal in the main energy consumption significantly reduces, while the proportion of renewable energy sources such as wind, solar, and biomass increases. It is worth noting that the proportion of nuclear is significantly rising. (3) The demand for electricity increases significantly in the Rcp2.6 scenario. This study reveals the specific directions for improving the energy structure of BRICS countries to achieve the global average temperature rise of no more than 2 degrees Celsius. It provides suggestions for the future policies of these countries' governments. In addition, BRICS countries should focus on observing changes in some factors, such as the increase in nuclear energy consumption and the increase in electricity demand. The reasons behind the changes in these factors are the direction of future research.

How can emerging economies meet development and climate goals in the transport-energy system? Modelling co-developed scenarios in Kenya using a socio-technical approach

Transport-energy transitions pose complex challenges that have been extensively studied in high-income countries in response to national mandates for climate action. Low- and middle-income countries, however, have low but rapidly growing motorisation rates and face very different challenges in adopting new technologies to foster economic development and ensure equitable access to clean transportation. Here, we present a set of narrative scenarios for the future of the Kenyan transport-energy system co-developed through engagement with 41 local experts and decision-makers. Through the co-development of a Kenyan transport-energy system model, we present a decision-support tool, populated with those scenarios, to assist policymakers at regional, national and international levels in building policy and investment pipelines to support low-carbon economic growth. We find that Kenya’s transport-energy system can meet both development and climate goals, but this demands strong policy support for efficient public transport and targeted support for road vehicle electrification. Increased support for non-motorised transport is essential to provide equitable access to services and economic opportunities. Favourable pathways result in significant e-mobility uptake, which is anticipated to increase electricity demand by 5%–56% from 2023 to 2040, relative to the IEA Kenya Energy Outlook’s Stated Policies scenario, representing a 2.7–3.9x increase in Kenya’s total electricity demand over the same period. From a macro-fiscal perspective, results show that e-mobility has two important consequences for Kenya. Firstly, under high e-mobility scenarios, there is a negative fiscal impact that taxation revenues from the sale of transport fuels reduce by up to 41% relative to the low e-mobility scenario (though, notably, they still increase marginally from the 2023 level because of increasing transport demand). Secondly, high e-mobility scenarios have a positive impact on balance of payments by reducing the fuel import bill by up to 69% relative to the low e-mobility baseline. This corresponds to a reduction in foreign exchange requirement of up to $4.2bn annually by 2050.

The Influence Of Organizational Culture, Organizational Commitment, And Organizational Communication On Employee Performance At PT. PLN (Persero) UP3 Sidoarjo

The increasing electricity demand requires electricity companies to achieve the specified targets in meeting stakeholder needs by improving the quality of Human Resources through various aspects. This study aims to determine the effect of organizational culture, organizational commitment, and organizational communication on employee performance at PT PLN (Persero) UP3 Sidoarjo. This research uses quantitative research. The population in this study were all employees of PT PLN (Persero) UP3 Sidoarjo as many as 95 employees. The sampling technique uses total sampling where all members of the population are taken as samples. The type of data used in this study is primary data obtained through distributing questionnaires and secondary data. The results of this study indicate that 1) organizational culture partially has a positive and significant effect on employee performance, 2) organizational commitment partially has a positive and significant effect on employee performance, 3) organizational communication partially has a positive and significant effect on employee performance, 4) organizational culture, organizational commitment, and organizational communication simultaneously have a positive and significant effect on employee performance at PT PLN (Persero) UP3 Sidoarjo

Customer Selection in the Conversion Program of LPG Stove to Induction Stove

The oversupply condition is due to differences in the current electricity supply which is higher than actual electricity demand. One option to increase demand is by converting LPG stoves into induction stoves. To achieve the conversion goal successfully, a customer selection process must be carried out to determine the conversion targets, aiming for a significant increase in electricity demand with the lowest investment costs. The main problem is the absence of a standard for determining conversion targets. The combination of clustering techniques and Multiple Criteria Decision Making (MCDM) analysis provides the best ideal solution to overcome these problems. The research included several main criteria for selecting customers that support the success factors of implementing a conversion program. The initial analysis involved clustering customers based on coordinates using the k-means method. By using the clustered customer data, the TOPSIS (Technique for Ordering Preference by Similarity to an Ideal Solution) method identified the best cluster based on general criteria. Additionally, using detailed customer data, the TOPSIS method also identified the ranking of customer priorities for each selected cluster. The recommended priority target customers are those with the greatest potential for additional kWh, the best level of electricity reliability, shortest distance to the cluster centroid, and supplied by feeders and distribution transformers with the largest reserve margin. The results indicated the difference in revenue improvement compared to the current method was US$ 16,291.31 per month. Investment costs were also reduced by US$ 166,084.80 compared to the current method. The other results were a reduction in operational costs, improvement in average reliability according to the SAIDI SAIFI, improvement in the average operation-hours, and an improvement in the distribution transformer average reserve margins.

Life cycle assessment and life cycle costing approach for building decarbonization by design choices: A case study

AbstractDecarbonizing the urban environment has two significant challenges: Increasing electricity demand due to the electrification of space heating and increased renewables share in the electricity supply. The European Commission defines a new grid support mechanism for peak‐shaving products in renewed Electricity Market Design. This aims to enable a new market tool to stabilize electric supply and demand. This study examines a grid‐integrated thermal storage device's technical feasibility and economic performance to meet net zero building (nZEB) definitions. Alternative scenarios considering current national nZEB targets, present energy market options, and regulations are compared using the life cycle cost and the global warming potentials over the building lifetime. The results show that better building performance is possible even with a low investment increase of 6.7% compared to minimum building standards based on regulations. This enables older building cases to perform similarly with new building targets for Turkish National nZEB. Building electrification using heat pumps and thermal storage systems gives similar economic performances in the long term when a dynamic electricity tariff is available. Adapting the life cycle approach to decision‐making in construction contracts can lead to a 50% decrease in building emissions while staying within the same construction budget.

Review of Community Solar in the United States of America

Due to the increase in demand for electricity during the last several decades and the fact that demand has to be satisfied, a number of power plants that work by utilizing fossil fuel sources have been built to satisfy the increased demand for electricity. As a result of the excessive use of fossil fuel sources, global warming and climate change have become serious issues that affect the entire world. The rise of those issues has put pressure on the entire world to switch from the use of fossil fuel sources energy to the use of green sources energy to generate electricity. Out of various renewable energy sources (wind, hydro, geothermal, and biomass), photovoltaics based on free fuel solar energy has emerged as the technology for generating lowest cost electricity. In addition to utility scale solar farms and rooftop solar, community solar benefits flow to multiple customers such as individuals, businesses, nonprofits, and other groups. The purpose of this paper is to provide a comprehensive review of community solar. A new business model of community solar is proposed where a photovoltaic system and battery energy storage system can work as standalone power system. Further cost reduction can be achieved if the standalone local power network works on direct current (DC) in place of alternating current (AC) power.

Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challenges have required the development of fast and reliable tools for evaluating the operation of power systems. The load margin (LM) is an important index in evaluating the stability of power systems, but traditional methods for determining the LM consist of solving a set of differential-algebraic equations whose information may not always be available. Data-Driven techniques such as Artificial Neural Networks were developed to calculate and monitor LM, but may present unsatisfactory performance due to difficulty in generalization. Therefore, this article proposes a design method for Physics-Informed Neural Networks whose parameters will be tuned by bio-inspired algorithms in an optimization model. Physical knowledge regarding the operation of power systems is incorporated into the PINN training process. Case studies were carried out and discussed in the IEEE 68-bus system considering the N-1 criterion for disconnection of transmission lines. The PINN load margin results obtained by the proposed method showed lower error values for the Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) indices than the traditional training Levenberg-Marquard method.

Energy Management Strategy for DC Micro-Grid System with the Important Penetration of Renewable Energy

This paper presents an energy management strategy using a Stateflow controller related to DC microgrids with the important penetration of renewable energy. The increase in world electricity demand is one of the principal drivers of the exhaustion of fossil fuels and increased greenhouse gas emissions. To solve these problems, several countries have adopted actions for widespread renewable energy deployment, which includes wind energy, solar power, biomass power, tidal, and hydropower. These sources are considered as significant in delivering clean energy and reducing greenhouse gas emissions for sustainable improvement. As these sources play an increasingly vital role in the global energy landscape, the efficient management of these intermittent sources is essential for grid stability and sustainability. This paper aimed to develop an energy management strategy for DC microgrids to supply power to a DC microgrid system. The main objective of this paper was to implement an energy management system to ensure the proper operation of DC microgrid systems utilizing Simulink blocks available in MATLAB/Simulink 2020b software. The simulation results demonstrated that the developed energy management algorithm was unconditionally reliable, ensuring the proper operation of the microgrid systems. Additionally, the results demonstrated that the energy management strategy exhibited robust performance across different scenarios, effectively balancing energy generation and consumption while ensuring the reliable operation of the microgrid system. Moreover, the developed algorithm model presents another advantage, as it enables users to access and to change any control parameters within the DC microgrid. By comparing these results with the literature, the developed energy management algorithm provides safety and the automatic control of the microgrid.

Assessing compounding climate-related stresses and development pathways on the power sector in the central U.S.

Future configurations of the power system in the central region of the USA are dependent on relative costs of alternative power generation technologies, energy and environmental policies, and multiple climate-induced stresses. Higher demand in the summer months combined with compounding supply shocks in several power generation technologies can potentially cause a “perfect storm” leading to failure of the power system. Potential future climate stress must be incorporated in investment decisions and energy system planning and operation. We assess how projected future climate impacts on the power system would affect alternative pathways for the electricity sector considering a broad range of generation technologies and changes in demand. We calculate a “potential supply gap” metric for each pathway, system component, and sub-region of the US Heartland due to climate-induced effects on electricity demand and power generation. Potential supply gaps range from 5% in the North Central region under mild changes in climate to 21% in the Lakes-Mid Atlantic region under more severe climate change. We find increases in electricity demand to be more important in determining the size of the potential supply gap than stresses on power generation, while larger shares of renewables in the power system contribute to lower supply gaps. Our results provide a first step toward considering systemic climate impacts that may require changes in managing the grid or on potential additional capacity/reserves that may be needed.

Permanent VS Transitory Shocks to Electricity Consumption: Panel Evidence from 19 African Countries

ABSTRACT: Considering Africa's increasing electricity demands and the pivotal role of electricity in socio-economic development, this study addresses a research gap in understanding the stochastic properties of per capita electricity consumption in African contexts. Through examining the integration properties of per capita electricity consumption across 19 African countries, the research determines whether 'shocks' yield transitory or permanent effects, aiding policy formulation and future demand forecasting. Annual electric power consumption data (1971 – 2014) (kWh per capita) from the WDI across 19 African countries are used. Our methodology starts with formally testing for cross-sectional dependence (CSD) using various tests to avoid potential size distortions. Subsequently, we employ the novel panel stationarity test of Nazlioglu and Karul (2017), a Fourier KPSS panel stationarity test. The robustness of the analysis is enriched by incorporating the Hadri and Kurozumi (2011) panel stationarity test and Nazlioglu et al. (2021) new panel stationary tests. The CSD test results reveal mutual dependency in electricity consumption among the sampled African countries, underscoring the need for a panel testing methodology that accommodates CSD. The results of the panel stationarity tests that allow CSD and structural breaks—specifically, the Nazlioglu and Karul (2017) test, which models structural breaks as a gradual process—reject the null hypothesis of trend stationarity for both single frequencies, i.e., k =1, and 2. Similarly, all new, complementary panel stationarity tests from Nazlioglu et al. (2021) reject the null hypothesis of stationarity for both frequencies. Collectively, the outcomes from various panel stationarity tests present strong evidence of nonstationary per capita electricity consumption within the sampled African countries. Additionally, the Nazlioglu and Karul panel stationarity test enables an analysis of the individual Fourier KPSS test for cross-sectional units in the panel. Of the 19 countries, only Togo does not reject the null hypothesis of stationarity for both frequencies. These individual country results align with the panel outcomes, providing compelling evidence for the nonstationarity of the per capita electricity consumption series across the investigated African nations. Accordingly, shocks to electricity consumption will have permanent effects, indicating that per capita electricity consumption will not return to its pre-shock equilibrium path. In this case, demand or consumption stabilizing policies will have long-run effects.

‘Greening’ an Oil Exporting Country: A Hydrogen, Wind and Gas Turbine Case Study

In the quest for achieving decarbonisation, it is essential for different sectors of the economy to collaborate and invest significantly. This study presents an innovative approach that merges technological insights with philosophical considerations at a national scale, with the intention of shaping the national policy and practice. The aim of this research is to assist in formulating decarbonisation strategies for intricate economies. Libya, a major oil exporter that can diversify its energy revenue sources, is used as the case study. However, the principles can be applied to develop decarbonisation strategies across the globe. The decarbonisation framework evaluated in this study encompasses wind-based renewable electricity, hydrogen, and gas turbine combined cycles. A comprehensive set of both official and unofficial national data was assembled, integrated, and analysed to conduct this study. The developed analytical model considers a variety of factors, including consumption in different sectors, geographical data, weather patterns, wind potential, and consumption trends, amongst others. When gaps and inconsistencies were encountered, reasonable assumptions and projections were used to bridge them. This model is seen as a valuable foundation for developing replacement scenarios that can realistically guide production and user engagement towards decarbonisation. The aim of this model is to maintain the advantages of the current energy consumption level, assuming a 2% growth rate, and to assess changes in energy consumption in a fully green economy. While some level of speculation is present in the results, important qualitative and quantitative insights emerge, with the key takeaway being the use of hydrogen and the anticipated considerable increase in electricity demand. Two scenarios were evaluated: achieving energy self-sufficiency and replacing current oil exports with hydrogen exports on an energy content basis. This study offers, for the first time, a quantitative perspective on the wind-based infrastructure needs resulting from the evaluation of the two scenarios. In the first scenario, energy requirements were based on replacing fossil fuels with renewable sources. In contrast, the second scenario included maintaining energy exports at levels like the past, substituting oil with hydrogen. The findings clearly demonstrate that this transition will demand great changes and substantial investments. The primary requirements identified are 20,529 or 34,199 km2 of land for wind turbine installations (for self-sufficiency and exports), and 44 single-shaft 600 MW combined-cycle hydrogen-fired gas turbines. This foundational analysis represents the commencement of the research, investment, and political agenda regarding the journey to achieving decarbonisation for a country.

Enlightening Bangladesh: Navigating power sector challenges through PPP excellence

As Bangladesh faces its current energy crisis, public-private partnerships (PPPs) emerge as a promising solution, bridging the strengths of both sectors toward a brighter, more electrified future. This research focuses on the challenges in Bangladesh’s power sector: increasing electricity demand and the imperative for a consistent supply of renewable energy sources. The research employs content analysis, exploring various aspects, including policy documents, regulatory frameworks, stakeholder engagements, and resource assessments, with a specific focus on three key variables: regulatory framework, stakeholder engagement, and informed policymaking. Drawing on the ‘resource-based view’ theory, the study emphasizes the significance of ‘mitigating resource risks’ through ‘resource assessment.’ Empirical support is derived from an extensive review of literature in reputable journals and research articles, enhancing the research’s credibility with real-world evidence. The study provides a practical roadmap for stakeholders navigating Bangladesh’s power sector, addressing energy challenges, and promoting sustainability.

Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review

Urbanization increases electricity demand due to population growth and economic activity. To meet consumer’s demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand imbalance by accurately predicting the energy consumption. This paper presents a comprehensive literature review of forecasting methodologies used by researchers for energy consumption in smart buildings to meet future energy requirements. Different forecasting methods are being explored in both residential and non-residential buildings. The literature is further analyzed based on the dataset, types of load, prediction accuracy, and the evaluation metrics used. This work also focuses on the main challenges in energy forecasting due to load fluctuation, variability in weather, occupant behavior, and grid planning. The identified research gaps and the suitable methodology for prediction addressing the current issues are presented with reference to the available literature. The multivariate analysis in the suggested hybrid model ensures the learning of repeating patterns and features in the data to enhance the prediction accuracy.

ELECTRIC VEHICLES: STUDY OF ENERGY DEMAND AND IMPACT ON INFRASTRUCTURE DUE TO THE INSERTION OF ELECTRIC BUSES IN THE CITY OF SÃO PAULO

Buses have become essential for passenger transport, both within major cities and between cities. In 2020 March 12,815 buses were responsible for transporting 8.7 million people every day on 1,336 lines throughout the city of São Paulo. Means of transport, however, account for about a quarter of greenhouse gas emissions and the most growth-prospective modal. To this end, technologies such as electric vehicles are presented in order to comply with new legislation. To assess the real impact of the increase in the number of electric vehicles, it is necessary to evaluate the energy demand to be considered and the possible restructuring of the electricity grid. The quantification of the total bus demand for operation in the city of São Paulo was carried out in four distinct scenarios of electrification planning of the bus fleet resulting in the projection of increased electrical demand, reduction of CO2 emissions and the survey of the main bus garages in order to map the points of possible increase in electrical demand, which, in its worst case scenario, showed an increase of 0.39% compared to electricity generated by the State of São Paulo. The gradual replacement of current vehicles powered by combustion engines by electrified vehicles should contribute to sustainable development by reducing greenhouse gas emission rates in large urban centres.

Tax Regulation of Countries in the Framework of Greening Business (using the example of the Russian Federation and the EU)

In the past three decades, the role of environmental protection in the environmental and climate agenda, which shapes the trends of “green” economic development, has been continuously increasing. The purpose of this study is to examine this issue in the context of the experience of the European Union (EU) and regulatory principles of taxation to prevent the effects of climate change. Using the example of the EU and the Russian Federation, the reaction of a system for economic regulation of greenhouse gas emissions, which is at the initial stage, is considered. A SWOT-analysis of Russia’s “green” exports to the EU countries has been carried out, which contains forecasts of a decline in demand for Russian oil and oil products, coal, gas and metals and an increase in demand for electricity from low-carbon sources. Taking into account the extremely high carbon intensity of all Russian exports and its product and country structure, it is suggested that notable damage may occur after 2026 if the Carbon Border Adjustment Mechanism (CBAM) expands its product mix and scope. In the presence of significant risks for the Russian Federation in the context of hydrogen energy development, the drivers for increasing competitiveness and the corresponding positions of our country in the conditions of decarbonization are determined, PEST-analysis of the Russian economy on the way to the “green” market course is carried out. The practical significance of the study lies in the proposal to use the experience of tax regulation in the EU countries as part of greening business in the Russian economy.

Assessing Iran and its neighbors for prospects and challenges: The case of the electrical sector

Rapid growth in population and economic development in Iran and its neighboring countries has resulted in a dramatic increase in electricity demand over the past few years. A substantial amount of electricity must be produced to meet this growing demand in this region. By installing conventional fossil power plants, using renewable energy, and interconnecting with neighboring countries, this growing demand can be accomplished. The main aim of this research is to identify prospects and challenges in the electrical sector around Iran. To this end, this paper evaluates the existing and future state of the electrical sector in Iran and its neighboring countries. Despite the fact that there is still a long way to go in terms of renewable energy in this region, there are some promising targets for the future that will allow fossil fuels to be replaced effectively. Notwithstanding this, we should point out that a number of cross-border interconnection projects are being pursued by Iran and its neighbors in order to integrate their networks and supply this growing demand. Finally, this study presents an overview of the future prospects and challenges of electricity capacity expansion, cross-border interconnections, and electricity transmission corridors in this region. The review results provide comprehensive background knowledge to all the researchers in order to investigate further into the field. This paper assists energy policy makers regarding the development of sustainable energy planning.

Temperature sensitive electricity demand and policy implications for energy transition: a case study of Florida, USA

The demand for electricity is soaring, propelled not only by population and GDP growth but also the pressing effects of climate change. This study seeks to address the uncertainties surrounding future electricity demand by projecting monthly consumption in Florida, USA, taking into account diverse climate scenarios and their potential impacts. Our approach involves utilizing the degree-day method and constructing an energy consumption regression model grounded in historical data. Key variables, including population, employment, GDP, electricity prices, temperature, and daylight hours, are systematically analyzed. This model acts as the fundamental basis for forecasting future electricity needs in residential, commercial, and industrial sectors across the state of Florida up to the year 2050, considering different climate scenarios. Under the Representative Concentration Pathway (RCP) 4.5 scenario, the residential sector foresees a substantial 63% increase in electricity demand from 2001–2019 to 2050. Under the more extreme RCP 8.5 scenario, this surge climbs to 65%. Meanwhile, the commercial and industrial sectors are expected to witness a 47% and 54% upswing in demand under RCP 4.5 and RCP 8.5, respectively. Intriguingly, heightened demand for cooling during scorching summers outweighs the reduced need for heating in winter, particularly in the residential sector. The current renewable energy policies fall short of addressing the impending climate-driven surge in electricity demand. To combat this, our recommendation is the implementation of a Renewable Portfolio Standard, aimed at significantly enhancing the proportion of renewables in Florida's electricity mix. This paper concludes with a set of crucial policy recommendations, imperative for steering a sustainable transition to renewable energy and effectively managing the impacts of extreme heat on people's lives. These recommendations serve as a strategic roadmap for navigating the evolving landscape of electricity demand amidst the complex challenges posed by climate change.