Net Demand Research Articles

Integrated market scheduling with flexibility options

This work presents a generic optimization framework using advanced mixed-integer programming techniques to integrate day-ahead and balancing markets with distributed energy resources such as storage, electric vehicles, demand response, and Transmission and Distribution System Operators' coordination schemes. The day-ahead model determines optimal initial energy scheduling, while the balancing model optimizes energy and reserve products for three market designs with varying Transmission and Distribution System Operators’ coordination. The framework is applied to the interconnected Greek-Bulgarian-Romanian power system, considering 2030 installed capacities.The model outputs illustrate market coupling scenarios among Romania, Bulgaria, and Greece, highlighting clear price signals and distributed energy resources flexibility. Results reveal the significance of a diverse energy mix for energy security and show that Transmission and Distribution System Operators’ coordination significantly influences ancillary service prices, with reductions of up to 80 % in certain scenarios. Net demand values determine electricity flow direction. Flexibility providers like storage can cover up to 100 % of the upward congestion management requirements and 11 % of upward balancing energy, helping smooth energy allocation from intermittent renewables. The impact of electric vehicle penetration on generation scheduling is minimal. The proposed model offers valuable insights for system operators, market participants, and policymakers, enabling them to provide accurate price signals and optimize resource allocation.The integrated day-ahead and balancing market models support efficient renewable energy integration, emissions reduction, enhanced grid stability, and investment in low-carbon technologies. This aligns with the United Nations Framework Convention on Climate Change goals, contributing to the development of sustainable and resilient energy systems.

Forecasting Wholesale Electricity Market Prices Considering Bidding Conditions Using Price Sensitivity

This article proposes a sophisticated forecasting model to predict significant price volatility on the Japan Electric Power Exchange (JEPX) due to the growing influence of solar photovoltaics (PV) in the energy mix. As solar power generation continues to grow, it significantly impacts the daily and seasonal price trends in the market. This method employs a neural network that integrates daily prices, net electricity demand (total demand minus PV output), and underlying time‐series data to determine the objective variable, defined as the deviation from the average price over the past week. Incorporating price sensitivity data from JEPX increases the accuracy of the model. These data reflect how prices respond to market‐specified bid adjustments, including a variety of bidding, providing a nuanced understanding of market responses. The results demonstrate that adding multiple price sensitivities significantly improves the model's ability to detect price spikes and provides a robust tool for transmission and distribution system operators to manage risk and optimize their market strategies in an increasingly renewable energy‐dominated landscape. This approach not only addresses daily and seasonal price fluctuations but also aligns with broader sustainability goals as the share of solar PV generation continues to grow.

Behind-the-Meter Load and PV Disaggregation via Deep Spatiotemporal Graph Generative Sparse Coding With Capsule Network.

Nowadays, rooftop photovoltaic (PV) panels are getting enormous attention as clean and sustainable sources of energy due to the increasing energy demand, depreciating physical assets, and global environmental challenges. In residential areas, the large-scale integration of these generation resources influences the customer load profile and introduces uncertainty to the distribution system's net load. Since such resources are typically located behind the meter (BtM), an accurate estimation of BtM load and PV power will be crucial for distribution network operation. This article proposes the spatiotemporal graph sparse coding (SC) capsule network that incorporates SC into deep generative graph modeling and capsule networks for accurate BtM load and PV generation estimation. A set of neighboring residential units are modeled as a dynamic graph in which the edges represent the correlation among their net demands. A generative encoder-decoder model, i.e., spectral graph convolution (SGC) attention peephole long short-term memory (PLSTM), is devised to extract the highly nonlinear spatiotemporal patterns from the formed dynamic graph. Later, to enrich the latent space sparsity, a dictionary is learned in the hidden layer of the proposed encoder-decoder, and the corresponding sparse codes are procured. Such sparse representation is used by a capsule network to estimate the BtM PV generation and the load of the entire residential units. Experimental results on two real-world energy disaggregation (ED) datasets, Pecan Street and Ausgrid, demonstrate more than 9.8% and 6.3% root mean square error (RMSE) improvements in BtM PV and load estimation over the state-of-the-art, respectively.

Signal-devices management and data-driven evidential constraints based robust dispatch strategy of virtual power plant

Ensuring the safety and reliability of energy systems is very important in power grid operation. However, inherent intricacies and uncertainties of modern-day power systems, such as the mismatch between signal frequency and equipment response speed and the stochasticity associated with renewable energy and load forecasts, create significant challenges for generation dispatch planning. This study proposes a robust optimization approach with constraints based on signal-devices management and data-driven evidential distribution constraints. The first stage of this approach is to decompose and recombine the net power demand according to the Hilbert-Huang transform and device capability. The signal-device management constraints are then established based on this. The second stage formulates an evidential constraint based on data-driven evidential distribution and chance constraints in a distributionally robust optimization framework that caters to the stochasticity associated with renewable energy and load forecasts. The proposed model is validated on a virtual power plant to assess the approach’s efficacy for different dispatching scenarios. Penalty-based sensitivity analysis provides further insights into the proposed method’s performance for varying levels of CO2 emissions. Simulation results demonstrate that system flexibility becomes increasingly crucial for maintaining system stability and security as the penetration of renewable energy grows. Compared with chance constraint, the proposed data-driven evidential constraint effectively enables the optimization framework to handle stochasticity after sacrificing 0.62% more economic loss and 0.7% more environmental loss. Excessively high penalty parameters for CO2 do not promote economic development, resulting in 21.08% and 52.51% more economic and environmental losses without obvious environmental protection.

Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn

As space heating accounts for 54% of annual residential electricity consumption in Quebec, demand response programs specifically target load shifting through the automated control of thermostat setpoints during peak hours. On a district scale, varied thermostat preferences and setpoint override behaviors can have an impact on the success of the demand response program. This study examines two unique occupant types (Average, Tolerant) in terms of thermostat setpoint preferences as well as three different occupant Levels-of-Detail (LoDs) and analyzes their effects on the energy flexibility provided during demand response periods. For our baseline scenario, LoD 1, a static setpoint schedule is used and there is no control of the heat pump, while LoD 2 and LoD 3 incorporate thermostat setbacks during demand response events. LoD 2 assumes the occupant is comfortable within 2°C from the setpoint while LoD 3 allows the occupant to override the DR setbacks. We estimate the flexibility services provided by a ten-house residential community through the automated control of heat pumps during a three-month winter period, and we implement and simulate our study in CityLearn using reinforcement learning-based control for district-level energy management. When comparing LoD 3 to LoD 1, electricity cost was reduced by approximately 12% and net electricity consumption was reduced by approximately 17% during demand response periods. Likewise, we find that LoD 2 could overestimate savings in net electricity consumption, cost, and peak demand by 5% compared to LoD 3. The number of hours where the indoor temperature deviated more than ▪ from the setpoint occurred for less than 5% of the timesteps on average for the 10 buildings for LoD 3 while still achieving significant net electricity consumption reductions, thus highlighting that we can provide energy flexibility services to the grid while balancing occupant thermal comfort. Finally, the agents learned optimal decision-making that reduced the number of overrides across the training episodes for both Average and Tolerant occupants. We thus present a multi-agent framework as a means for addressing various occupant setpoint preferences and override behaviors for the control of heat pump systems at the neighborhood level.

Establishing the water resources implications for closing the land and water productivity gaps using remote sensing – A case study of sugarcane

ContextTwo of the key limitations for sustainably increasing agricultural production are the scarcity of land and fresh water resources. Establishing land and water productivity gaps is, therefore, essential for measuring how efficiently these resources are being utilised and assessing the scope for increasing feed and food production. Monitoring the productivity gaps at large scales or over time using field data is challenging and expensive. Remote sensing offers an alternative data source to reveal spatial and temporal variations in productivity. ObjectiveThis paper presents a framework that integrates remote sensing derived data and field data to assess (1) land and water productivity gaps, (2) bright spots – fields exhibiting land- and water productivity equal to or higher than the target, and (3) net irrigation water demand for increasing production. MethodsThe framework is developed and applied to the Xinavane sugarcane estate in Mozambique, demonstrating its practical application through systematic evaluation on a 6637 ha section of the estate divided by different irrigation application methods. ResultsThe results reveal that the productivity gap is the highest on fields irrigated by furrow (13.1 tonnes (ton) per ha), followed by sprinkler (12.6 ton/ha) and centre pivot (9.4 ton/ha). Bridging the productivity gap on the same cropland results in an increased sugarcane production of 12.5 % requiring 8.5 % additional irrigation water, whereas achieving the same production increase through irrigation expansion requires more blue water. ConclusionsThe analyses show that remote sensing provides a viable source of information to diagnose the productivity constraints and how bright spots can provide insights into the best field management practices to overcome them. The framework demonstrates its usefulness for policy makers and stakeholders to make informed decisions on the scarce blue water allocation for enhancing agricultural production.

Federated learning assisted distributed energy optimization

AbstractThe increased penetration of distributed energy resources and the adoption of sensing and control technologies are driving the transition from our current centralized electric grid to a distributed system controlled by multiple entities (agents). The transactive energy community serves as an established example of this transition. Distributed energy management approaches can effectively address the evolving grid's scalability, resilience, and privacy requirements. In this context, the accuracy of agents' estimations becomes crucial for the performance of distributed and multi‐agent decision‐making paradigms. This paper specifically focuses on integrating federated learning (FL) with the multi‐agent energy management procedure. FL is utilized to forecast agents' local energy generation and demand, aiming to accelerate the convergence of the distributed decision‐making process. To enhance energy aggregation in transactive energy communities, we propose an FL‐assisted distributed consensus + innovations approach. The results demonstrate that employing FL significantly reduces errors in predicting net power demand. The improved forecast accuracy, in turn, introduces less error in the distributed optimization process, thereby enhancing its convergence behaviour.

Climate policy stringency and trade in energy transition minerals: An analysis of response patterns

Climate policies directly affect cleaner energy technology markets, affecting price signals for greener electricity production. In turn, the manufacturing of energy technologies relies heavily on mineral inputs, posing challenges in securing mineral ores and concentrates either from local mines or overseas. This paper contributes to the mineral-energy literature by examining the impact of climate change policies on the net import demand for five essential minerals used in cleaner energy technologies: copper, aluminium, nickel, cobalt, and manganese. We examined country-level panel data from 33 countries from 1992 to 2015 to understand patterns in net imports relative to renewable generation. This metric allows the examination of technological improvements that contribute to more efficient use of imported mineral inputs in renewable energy systems. Results suggest that while both net import volumes and renewable generation have increased over time, net imports per renewable generation have continuously declined on average, suggesting that nations may have efficiently utilized imported resources to generate more than proportionate increases in cleaner energy. Moreover, results from a cross-sectional autoregressive distributed lag model show that the stringency of environmental policies significantly affects net mineral imports per renewable generation, with marked variations across minerals and countries. Notably, with stricter policies, import reliance on cobalt and aluminium per renewable energy increases in the short run and eventually declines in the long run. Import reliance for copper declines per unit of renewable energy in the short run but increases in the long run. These findings emphasize the need for tailored policies to navigate mineral trade dynamics effectively. The research contributes valuable insights to the ongoing discussions surrounding sustainable energy transitions and global mineral trade patterns.

Retail investors and ESG news

An important debate exists around the extent to which retail investors make sustainable investments and, if they do, why. We contribute to this debate by investigating the aggregate trading patterns of retail investors around a comprehensive sample of key environmental, social, and governance (ESG) news events for U.S. firms. We show that ESG news events appear to be an important factor in retail investors’ portfolio allocation decisions. Yet, inconsistent with arguments about retail investors’ nonpecuniary preferences, our evidence shows that retail investors mainly trade on this information when they deem it financially material to a company’s stock performance. We also find their net trading demand predicts abnormal returns in the subsample of financially material events, consistent with retail traders benefiting from incorporating ESG-related information into their decision-making when it influences firm value. Overall we conclude that the average U.S. retail investor cares about firms’ ESG activities but primarily to the extent these activities matter for company financial performance.

Operational Matrix Framework for Energy Balance Analysis for Early Stage Design of Complex Vessels

Considering vital ship systems or distributed ship service systems at the early stage of complex vessels is a challenging task. The recent UCL Network Block Approach aimed to enable ship designer to address ship systems design synthesis simultaneously as a logical network using MATLAB with a CPLEX Toolbox in MATLAB and representative piping, cabling, and trunking routings on the physical description of the ship using a proven CASD tool PARAMARINESURFCON. This was possible due to adopting a set of frameworks, as part of this comprehensive approach. The paper presents one of the frameworks: the Operational Matrix, to formulate distributed ship service systems network in the early stage design of complex vessels. The application of the framework could take on many forms and can be manipulated to suit a specific distributed ship service system’s design. In this paper, a tutorial is given, leading from the simplest application of the Operational Matrix Framework to an example of a complex Operational Matrix application for the 3D multiplex submarine systems problem. The use of the proposed Operational Matrix Framework is shown to reveal the relationship between objective functions, constraints, bounds, and solutions of that linear programming formulation. The Operational Matrix Framework can enable the solvers (CPLEX Toolbox in MATLAB) to be very efficient in advancing early stage ship design applications. The Framework could be developed further for investigating the analysis of energy balances for new systems to achieve net zero energy demands for future naval vessels.

Hybrid energy storage sizing in energy hubs: A continuous spectrum splitting approach

Renewable powered energy hub is a promising way to realize efficient use of renewable resources through multi-energy integration. To cope with the fluctuation of renewable power at different timescales, long-term and short-term energy storage devices are essential. This paper proposes a frequency-domain approach to determine the appropriate capacities of hydrogen and battery energy storage units in an electricity–hydrogen–heat integrated energy hub. The net demand is mapped to the frequency domain via discrete Fourier transformation (DFT). A continuous spectrum splitting method is developed to allocate the frequency components among generator, hydrogen storage and battery storage. Compared with the time-domain method, it makes better use of the spectral characteristics of the year-round data, leading to robust sizing results without complex modeling of uncertainty. In contrast to the existing cut-off frequency method which assigns high-frequency components to battery storage and low-frequency components to generators according to a cut-off frequency, the proposed framework allows a single spectrum component to be allocated to multiple system devices, which not only makes the optimization model convex but also improves optimality. In addition, the spectrum clustering process further reduces the number of core variables and the computational efficiency is high. The rationality of the sizing results is verified through online operation, where the proposed method achieves an average of 1.93% total load shedding rate compared with 4.42% of the time-domain method, verifying the operation reliability under uncertainty. While fully utilizing the spectrum characteristics, the investment cost is about 22% less than the cut-off frequency result.

Energy Performance Analysis of the Renovation Process in an Italian Cultural Heritage Building

Renovating buildings with cultural heritage significance is an important step toward achieving sustainability in our cities. The benefits are not only energy-related but also encompass social aspects that make these renovations a high apriority. The present work investigates the renovation process of a cultural heritage building in the Municipality of Trento in Italy, specifically focusing on achieving energy savings and renewable energy integration by implementing various renovation actions. These renovation actions include improvements to the building envelope, such as roof insulation and window replacements. Additionally, the renovation actions for active systems involve the installation of a ground-source heat pump for heating/cooling coupled with a borehole thermal energy storage system, which is an innovative technology for the renovation of cultural heritage buildings. The electrical systems of the building are upgraded through the addition of standard rooftop photovoltaics, innovative building-integrated photovoltaics (shingles), and the installation of an LED lighting system. The baseline and the renovation scenarios are studied using the dynamic simulation tool INTEMA.building, written in the programming language Modelica. This tool simulates both the building envelope and the energy systems with a high level of detail, using advanced control systems and adjustable time steps. According to the simulation analysis, the primary energy demand is reduced by 30.49%, the final energy demand by 36.74%, and the net electricity demand by 8.72%. Results from this study can be useful to interested stakeholders (e.g., building owners, architects, construction companies, public agents, and urban planners) dealing with the renovation of cultural heritage and protected buildings. Also, the results can be exploited for estimating energy savings by applying advanced renovation strategies for cultural heritage buildings.

Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm

As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or winter, by flexibly responding to the grid demands. Indoor air temperature set-point resetting is a typical passive demand response (DR) control strategy that have potentials to be widely implemented by inverter air conditioners in residential buildings. However, in the existing small and medium-size public buildings, the compressor motor of HVAC systems mostly does not allow for stepless speed changes. This means that the indoor temperature set-point resetting strategy may results in frequent on/off switching of the chiller plant, leading to a compromised lifespan. Although rooftop photovoltaic systems (PVs) are expected to be widely adopted in new buildings, their potential for peak load shaving or valley load filling has received limited investigation. This study proposes a grey-box model-based demand side management (DSM) method for variable air volume systems (VAVs) and rooftop PVs in public buildings. The DSM method utilizes the particle swarm optimization (PSO) algorithm to determine optimal indoor air temperature and/or evaporator outlet water temperature set-point schedules for the VAVs, as well as optimal charging/discharging schedules for the storage battery of rooftop PVs. This approach aims to reduce the peak power demands of the integrated system without causing severe thermal discomfort, increasing chiller mechanical costs (refers to frequent on/off switching of the chiller plant in this paper), or overcharging/over-discharging of storage batteries. The results of case studies demonstrate that the strategy of co-resetting indoor air temperature – evaporator outlet water temperature set-points outperforms the indoor air temperature set-point resetting strategy in terms of both peak power demands reduction and mechanical cost saving when applied to the VAVs in low performance buildings. In comparison, for the VAVs installed in normal buildings, both the strategies yield significant decreases in peak power demands and mechanical cost by fully utilizing the thermal capacity of the building internal thermal mass. Furthermore, by implementing the DSM method for rooftop PVs, the integrated system can achieve zero or near-zero net power demands during the DR periods. The proposed DSM method can also be utilized in valley load filling programs to facilitate the accommodation of renewable energy.

The role of intermediaries in derivatives markets: Evidence from VIX options

Consistent with models of intermediaries who absorb demand pressure from end-users and respond by changing prices, net option demand is positively related to option prices in the market for VIX puts and calls. VIX and SPX option markets are integrated: market-makers absorb end-users’ net long volatility positions and net demand affects prices across markets. Net demand changes in the most liquid markets result in spillover effects between the VIX and SPX markets. A dynamic characterization of prices and net demand suggests that VIX option markets and the SPX put market are integrated, while the SPX call market is more segregated.

Impact of specific liquidity shocks on the bank's solvency

PurposeThis study aims to demonstrate and measure the impact of liquidity shocks on a bank’s solvency, especially when the bank does not hold sufficient liquid assets.Design/methodology/approachThe proposed model is an extension of Merton’s (1974) model. It assesses the bank’s probability of default over one or two (short) periods relative to liquidity shocks. The shock scenarios are materialised by different net demands for the withdrawal of funds (NDWF) and may lead the bank to sell illiquid assets at a depreciated value. We consider the possibility of second-round effects at the beginning of the second period by introducing the probability of their occurrence. This probability depends on the proportion of illiquid assets put up for sale following the initial shock in different dependency scenarios.FindingsWe observe a positive relationship between the initial NDWF and the bank’s probability of default (particularly over the second period, which is conditional on the second-round effects). However, this relationship is not linear, and a significant proportion of liquid assets makes it possible to attenuate or even eliminate the effects of shock scenarios on bank solvency.Practical implicationsThe proposed model enables banks to determine the necessary level of liquid assets, allowing them to resist (i.e. remain solvent) different liquidity shock scenarios for both periods (including eventual second-round effects) under the assumptions considered. Therefore, it can contribute to complementing or improving current internal liquidity adequacy assessment processes (ILAAPs).Originality/valueThe proposed microprudential approach consists of measuring the impact of liquidity risk on a bank’s solvency, complementing the current prudential framework in which these two topics are treated separately. It also complements the existing literature, in which the impact of liquidity risk on solvency risk has not been sufficiently studied. Finally, our model allows banks to manage liquidity using a solvency approach.

Localized management of distributed flexible energy resources

The integration of distributed energy resources continues to be a challenge for the traditional electricity grid. Aside from additional variability in the net power demand, there is often a temporal discrepancy between generation and consumption schedules which can exacerbate demand peaks and troughs, leading to system overloading, voltage violations, and a low system load factor. This study proposes a control system approach to unify the management of the different types of flexible energy resources through Localized Demand Control. In this system, all flexible energy resources are coordinated using a power line carrier frequency signal to follow a preferred demand curve at the distribution transformer level. The system’s functionality has been confirmed in an actual 5-house microgrid without adverse effects on end-user comfort. Meanwhile, scaled-up simulation studies have shown improvements in the system load factor by as much as 189%, and increased hosting capacity by 40% for electric vehicles and 16% for solar photovoltaic panels.

Influence of the photovoltaic generation in a small distribution network

In this paper are presented the results of a study carried out with the data of a electrical energy distribution network in a village in the region of La Rioja (Spain). In this distribution network, property of a small utility, is connected a photovoltaic power plant with a capacity comparable with the total load connected to the network in some hours. In the study there have been used actual load and generation data, discussing the influence of the photovoltaic generation on the net demand of the village over a period of two years (2009-2010). This study is a first phase for the application of demand side management actions for adjusting the load curves with generation curves. In a second phase the influence of new distributed generation facilities, based on renewable sources (wind, biomass, cogeneration, etc.) will be considered in order to minimize the dependence of the village from external supplies.

Time Aggregation in Presence of Multiple Variable Energy Resources

This paper proposes a new time aggregation method that can properly reflect the spatial aspect of the data sources alongside their temporal behaviour when multiple variable energy resources are involved. Each representative period, constructed by the proposed method, can include more than one representative day, which, in itself, represents a group of data sources that are highly associated with each other. It also preserves the spatial relationship among data sources as a whole by constructing a hybrid centroid. Two case studies, each with different data sources and networks, with and without energy storage, are considered for this study. In the second case, we add an storage to reflect the significance of preserving shape of patterns in the representative periods. The duration curves of wind farms' and solar farms' generations, electricity demand, and net demand are used as data-based evaluation indices to compare the performance of the proposed method against other approaches. In addition, an IEEE 24-bus and a 6-bus test system are considered for the case studies. Two model-based evaluation indices are used to measure the performance of the clustering methods, including the power flow of all the lines in the system and the annual operation cost of the system. In addition, the performance of the proposed method in the presence of energy storage is evaluated and compared with existing methods.

Mitigating renewables curtailment and carbon emissions in California through water sector demand flexibility

Increased renewable energy deployment in places like California have led to more frequent curtailment of these resources when their generation exceeds simultaneous demand for electricity, resulting in wasted opportunities for electric grid decarbonization. Public potable water distribution systems, which account for approximately 1.2% of statewide electricity demand, may be able to shift the timing of their operations by filling their storage tanks when renewable energy is prevalent to help the California energy grid reduce renewables curtailment, grid emissions, and peak energy demand. We simulate the water demand, pumping, and energy demands of 702 public water distribution systems serving 84% of Californian residents under eight different energy pricing and emissions minimization scenarios. We find that the potable water distribution sector in California has the flexibility to shift its energy demands by up to 1071 GWh annually, reduce renewable energy curtailment by up to 68%, reduce the net demand peak by up to 321 MW, and avoid 330,627mTCO2e of emissions. The scenarios that led to the greatest load shifting and emissions reductions included time-of-use energy rates that incentivize consumption during the middle of the day without a demand charge. The findings of this work not only highlight the potential of the water sector to help meet future energy and emissions goals, but also how the energy sector can best incentivize this shift.

Mapping Local Synergies: Spatio-Temporal Analysis of Switzerland’s Waste Heat Potentials vs. Heat Demand

As nations transition to renewable energy, making use of waste heat becomes crucial to combat climate change. This study focused on quantifying Switzerland’s waste heat potential from industrial processes and waste-to-energy facilities, using diverse methodologies tailored to facility characteristics and data availability. We assessed potential waste heat utilization by comparing local heat supply and demand, creating comprehensive heat-balance maps considering different temperature levels and seasonal fluctuations. Results revealed a substantial annual waste heat potential of 37 TWh, with almost half (17 TWh) below 45 °C, primarily from wastewater. Heat between 45 °C and 70 °C, ideal, e.g., for greenhouse heating, is mainly available from solid waste incineration plants, while industries contributed to waste heat supply exceeding 150 °C. In contrast to heat demand, seasonal variations in heat supply were small, with a 12% winter decrease. Analyzing heat demand versus supply unveiled local and seasonal disparities. Most municipalities had a net excess heat demand (totaling 89 TWh). Additionally, waste heat could not satisfy 8 TWh of industrial process heat demand exceeding 400 °C, emphasizing reliance on primary energy sources for higher-temperature heat. Targeted strategies are essential for effective waste heat utilization, especially tapping into low-temperature sources. Integrating these sources with low-carbon technologies can pave the way to a sustainable energy future.