On-site Renewable Energy Sources Research Articles

Reinforcement Learning for EV Fleet Smart Charging with On-Site Renewable Energy Sources

In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK’s target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions.

Risk-averse energy dispatch for hybrid energy refueling stations considering Boundedly rational mixed user equilibrium and operational uncertainties

Nowadays, charging stations, hydrogen refueling stations, and natural gas refueling stations are being integrated into hybrid energy refueling stations (HERSs) to save investment costs and offer diverse refueling services altogether. This research proposes a risk-averse energy dispatch strategy for HERSs, incorporating bounded user rationality and operational uncertainties. A boundedly rational mixed user equilibrium model is proposed to capture the mobility of heterogeneous traffic flow, including electric vehicles (EVs), hydrogen fuel cell vehicles (HFCVs), and natural gas vehicles (NGVs), each with distinct refueling demands characterized by bounded decision-making rationality. A grid-connected HERS model integrated with carbon capture and storage and on-site renewable energy sources is designed to provide sustainable refueling services to EVs, HFCVs, and NGVs. Furthermore, an HERS operator aggregates multiple HERSs to participate in the day-ahead and real-time electricity markets. The optimal equipment operation and energy bidding strategies for the HERS operator are solved using a risk-averse approach based on conditional value-at-risk, addressing operational uncertainties from heterogeneous refueling demand, electricity markets, and renewable generation. Numerical results have validated the effectiveness of the proposed method in promoting net-zero emissions, securing economic benefits, and mitigating risks associated with operational uncertainties.

The AI circular hydrogen economist: Hydrogen supply chain design via hierarchical deep multi-agent reinforcement learning

Hydrogen supply chain (HSC) consists of various production, conditioning, transportation, storage, and distribution processes, all of which require extensive computational resources for precise modelling. In this context, artificial intelligence (AI) is emerging as a pivotal tool for addressing model-based decision-making challenges, courtesy of its rapid and efficient computational capabilities. This paper proposes a comprehensive HSC model consisting of an economic policy planner, a wholesale hydrogen market, a power supply system, a hydrogen distribution system (HDS), and hydrogen refueling stations (HRSs). It leverages an AI circular hydrogen economist approach based on a hierarchical deep multi-agent reinforcement learning (MARL) algorithm, offering a new alternative to traditional multi-objective bi-level optimization platforms. The model incorporates green, blue, and gray hydrogen production processes as viable hydrogen production pathways, with the hierarchical MARL’s agents representing the HDS and HRSs at two decision-making levels. Energy requirements are met through a combination of on-site renewable energy sources, the main power grid, or distributed power generation systems. Several HSC scenarios are examined with respect to various combinations of green hydrogen supply rates and carbon credits granted to them under optimum conditions. The results showed that the developed hierarchical MARL has the potential to replace mathematical programming (MP), uncovering a new economic-environmental trade-off between profit of HDS and operational costs of HRSs. Notably, green hydrogen transactions exponentially increase within the supply chain as the carbon credits exceed $1 per kilogram of hydrogen. While this research focuses on optimizing daily operations within the HSC, future efforts can aim to extend this optimization to forecasted annual operations.

Deep retrofitting toward net zero energy building in the Mediterranean region: A case study of Albania

Like many other Mediterranean countries, Albania faces unique challenges and opportunities to achieve an efficient and fully decarbonized household sector by applying real energy efficiency measures and numbers toward nearly zero-energy buildings. The study findings showed that nZEB in 2030 can be achieved by combining active and passive energy efficiency measures. Behind the study's state-of-the-art stands a multivariable regression analysis of both electricity consumption and electricity generation executed for three validated consecutive years, 2021, 2022, and 2023, including demand side (electricity bills) and supply side generation provided from a nearby existing onsite PV with an installed capacity of 5.5 kWp. A good mathematical correlation is carried out using Durbin – Watson criteria on statistics using RETScreen software to correlate electricity consumption and generation as a function of weather parameters for the tested household and selected region. The goals to meet the nZEB concept require the insulation of the existing exterior wall and window replacements in compliance with the Energy Performance Methodology on Building and installing rooftop Solar Water Heating (SWH) panels of 3.73 m2 as expostulated in scenario 7. The simulation results show that integrating a rooftop PV system with an installed capacity of 5.5 kWp would provide enough electricity to convert residential buildings into Positive Energy Buildings in 2050 based on the selected dwelling and location (Mediterranean region). At the national level, the proposed model would reduce the national annual electricity import level by 1.64 %, saving annually around €31,021,162 and reducing at least 0.068 MtCO2 per year. Subsidizing passive energy efficiency measures at a rate of 3 % per year coupled with onsite renewable energy sources schemes and other incentivization pathways from the government and other interested parties at a rate of 80 % in the roadmap towards nZEB and PEB is mandatory.

Solar Charging Station for Easy Bikes in Bangladesh: Validation and Post-Model Evaluation

The significance of renewable sources of energy is enormous in reducing the demand, use, and also price of fossil fuels following recent advances in related technologies allowing for the development of solar-powered cars. The number of users of electric vehicles (EVs) is increasing rapidly day by day all over the world. Due to the growing number of users of EVs, our existing electrical network is facing a lack of electric power because of the overload. As the number of electric and hybrid vehicles increases, efficient and integrated charging stations must be developed to solve the overload issues in existing networks. To address this, the paper proposes using onsite renewable energy sources such as solar energy and existing networks to improve the efficiency and effectiveness of charging stations. Combining photovoltaic with battery storage in a grid-connected system ensures the reliable functioning of the hybrid charging station. The paper focuses on the post-model evaluation, charging station distribution and location, business model, limitations, and software simulation and corresponding calculation. The results show that hybrid-powered charging stations can be economical, dependable, and efficient enough to fulfill the various demands of electric vehicle loads. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 1-9

Mapping the Potential of Zero-Energy Building in Greece Using Roof Photovoltaics

The present study investigates the incorporation of renewable rooftop photovoltaic systems in fully electrified residential buildings and estimates the zero-energy demand building potential in relation to the climatic data of Greece. Specifically, the aim of the analysis is to calculate the maximum possible number of stories and therefore the total building height for a complete transformation to zero-net-energy building. The energy analysis, which is conducted using the DesignBuilder software, focuses on single-floor up to seven-story buildings. The importance of the present work lies in the acknowledgment of the diversity of the Greek residential sector, the adherence to national energy policies, and the European goal of fully electrified buildings. The examined case studies are equipped with electrically driven air-to-air heat pumps serving the space heating and cooling demands and with an air-to-water heat pump covering the domestic hot water requirements. The investigated locations are the four main cities of Greece, Athens, Thessaloniki, Chania, and Kastoria, which represent the country’s four climatic categories. The conducted analysis allows for the mapping of the zero-energy building potential for the climatic data of Greece, demonstrating the possibility of striking a positive building energy balance through the integration of on-site renewable energy sources and the production of necessary electrical energy. The novelty of the present work lies in the identification of a key factor, namely, the building height, which determines the feasibility of transforming multifamily buildings into zero-energy buildings. According to the analysis results, the critical number of stories is calculated at six for Chania, five for Athens, four for Thessaloniki, and two for Kastoria. Regarding a three-story residential building, the incorporation of a renewable photovoltaic system can result in an annual surplus electricity production of 13,741 kWh (Chania), 10,424 kWh (Athens), and 6931 kWh (Thessaloniki), and a corresponding coverage of 100% (Chania), 69.0% (Athens), 38.9% (Thessaloniki) and 0% (Kastoria).

Optimizing integrated lot sizing and production scheduling in flexible flow line systems with energy scheme: A two level approach based on reinforcement learning

Many production environments are faced with the need to simultaneously determine the planning of lot sizing and the scheduling of production sequences while ensuring cost minimization. This issue becomes even more complex when integrating multiple energy sources with the goal of a low-carbon economy. To address this challenge, this paper proposes an integrated lot sizing and flexible flow line production scheduling model under a time-of-use pricing scheme. In addition, the model takes into account conventional grid power, on-site renewable energy sources, and an energy storage system. The associated objective function is solved adopting a two-level approach to optimize energy costs while maintaining production throughput and meeting customer demand. The implementation relies on reinforcement learning capabilities to tackle complexity issues. The proposed approach is evaluated on a benchmark case and its results are compared with those obtained with First-In-First-Out heuristic, genetic algorithm and CPLEX. These results highlight the promising aspect of the proposed approach in terms of performance.

Expanding Building Archetypes to Estimate the Indoor Environment Thermal Storage Capacity in the Danish Building Stock when Performing Demand Response

Short-term demand response leveraging the energy flexibility of the building stock is a key solution to decarbonise and ensure the reliability and sustainability of smart energy grids dominated by intermittent renewable energy sources. Most of the building demand response capacity depends on different forms of energy storage in the built environment. In addition to electric batteries, hot water storage tanks, ground source heat exchanger or dispatchable on-site renewable energy sources, buildings possess a tremendous thermal storage capacity within the thermal inertia of their structure and indoor environment. The latter can easily be harnessed with heating/cooling indoor temperature setpoint modulation strategies. However, there is a clear lack of large-scale estimates of this indoor environment thermal storage capacity in the different building stocks. To tackle this issue, detailed dynamic numerical models of the different building typologies in European countries are generated to assess the potential of demand response strategies at nationwide levels. The current paper presents the preliminary results on the thermal storage capacity in the Danish building stock when performing heating temperature setpoint modulation. These results are based on estimates of the effective thermal inertia of the different building archetypes in Denmark. They show that the Danish building stock contains immense thermal storage capacity, which is comparable to all combined batteries in a large fleet of electric vehicles or all industrial-size storage tanks in district heating plants.

Quantifying the environmental implication of cotton-Fiber-based Nanocrystalline cellulose: A life-cycle assessment

Considering the increasing demand for nanocrystalline-cellulose in the industry, due to its exceptional physical and biological properties, cheaper and more efficient production processes are sought. Addressing environmental concerns, especially within the framework of EU policies, this study employs Life Cycle Assessment (LCA) to evaluate the environmental performance of a novel nanocrystalline-cellulose production procedure, encompassing biomass depolymerization, rinsing, and bleaching. The LCA aims to identify environmental hotspots, explore mitigation measures, and enables comparisons with other LCA studies on nanocrystalline-cellulose. The results are calculated and reported for 19 environmental impact categories, using the ReCiPe 2016 impact assessment method. The production of 1 kg of dry nanocrystalline-cellulose using the novel process emits 63.7 kg CO2 equivalent, which is lower than the literature average (68 kg CO2 equivalent). The solvent (e.g. diethylene glycol) is the major contributor to the global warming potential and fossil-fuel depletion potential in the product stage of the nanocellulose, while the electricity requirements and glycerin represent environmental hotspots regarding 15 of the 19 impact categories assessed. In terms of the water-consumption potential, the environmental hotspot is production of raw materials (e.g. cotton fibers). Electricity contributes more than 50 % of the burden to the impact categories associated with ionizing radiation, the pollution of aquatic ecosystems and human toxicity related to cancer. It also holds a significant share of the burdens for terrestrial acidification (48 % of the impact), the formation of fine particulate matter (46 % of the impact), and human toxicity related to non-cancer diseases (37 % of the impact). This underscores the importance of optimizing the production process, possibly through upscaling. Additionally, incorporating on-site renewable energy sources and utilizing biomass-derived diethylene glycol can enhance the environmental performance of nanocrystalline-cellulose.

Performance Assessment of Two Different Phase Change Materials for Thermal Energy Storage in Building Envelopes

To meet the 2050 EU decarbonization goals, there is a need for new and innovative ideas to increase energy efficiency, which includes reducing the energy consumption of buildings and increasing the use of on-site renewable energy sources. One possible solution for achieving efficient thermal energy transition in the building sector is to assign new functionalities to the building envelope. The building envelope can function as a thermal energy storage system, which can help compensate for irregularities in solar energy availability. This can be accomplished by utilizing phase change materials as the energy storage medium in the building envelope. In this paper, two phase change materials with different melting temperatures of 21 °C and 28 °C are compared for their application in a dynamic solar building envelope. Both experimental and numerical studies were conducted within the scope of this study. The laboratory testing involved simulating the conditions of the four seasons through steady-state and dynamic experiments. The performance of the phase change materials was evaluated using a small-scale PASLINK test stand that imitates indoor and outdoor conditions. A numerical model of a small-scale building envelope was created using data from laboratory tests. The purpose of this model was to investigate how the tested phase change materials perform under different climate conditions. The experimental findings show that RT21HC is better at storing thermal energy in the PCM and releasing it into the indoor area than RT28HC. On the other hand, the numerical simulation results demonstrate that RT28HC has an advantage in terms of thermal storage capacity in climates found in Southern Europe, as it prevents overheating of the room.

A Review on Mode Transition Strategies between Grid-Connected and Standalone Operation of Voltage Source Inverters-Based Microgrids

Microgrids technologies are seen as a cost effective and reliable solution to handle numerous challenges, mainly related to climate change and power demand increase. This is mainly due to their potential for integrating available on-site renewable energy sources and their flexibility and scalability. The particularity of microgrids is related to their capacity to operate in synchronization with the main grid or in islanded mode to secure the power supply of nearby end-users after a grid failure thanks to storage solutions and an intelligent control system. The most critical operating case occurs when a sudden transition from grid-connected (GC) to stand-alone operation (SA) happens. During the transition, the system experiences abrupt changes that can result in a malfunction of the control system and a possible failure of the power system. The transition issue attracted considerable attention from researchers. Indeed, many research works are proposed to address this issue by proposing detection and transition techniques that ensure a smooth transition at the islanding time. Although there are several approaches to dealing with this issue, a categorization of the proposed methods in the literature and their differences is useful to assist engineers and researchers working on this topic. Thus, this study proposes a comprehensive review to summarize these approaches and point out their advantages and limitations.

Power-to-X in energy hubs: A Danish case study of renewable fuel production

The European Commission recently proposed requirements for the production of renewable fuels as these are required to decarbonize the hard-to-electrify parts of the industrial and heavy transport sectors. Power-to-X (P2X) energy hubs enable efficient synergies between energy infrastructures, production facilities, and storage options. In this study, we explore the optimal operation of an energy hub by leveraging the flexibility of P2X, including hydrogen, methanol, and ammonia synthesizers by analyzing potential revenue streams such as the day-ahead and ancillary services markets. We propose EnerHub2X, a mixed-integer linear program that maximizes the hub’s profit based on current market prices, considering the technical constraints of P2X, such as unit commitment and non-linear efficiencies. We investigate a representative Danish energy hub and find that without price incentives, it mainly sells renewable electricity and produces compressed hydrogen. A sufficient amount of renewable ammonia and methanol is only produced by adding a price premium of about 50% (0.16 €/kg) to the conventional fuel prices. To utilize production efficiently, on-site renewable energy sources and P2X must be carefully aligned. We show that renewable power purchase agreements can provide flexibility while complying with the rules set by the European Commission.

Guest editorial: Application of cloud energy storage systems in power systems

Guest editorial: Application of cloud energy storage systems in power systems

Modelling and simulation of a thermal storage system based on phase change materials integrated in a tertiary building

Decarbonization of the building sector is one of the key challenges to achieve the ambitious goal of carbon neutrality by 2050, established in the European Green Deal. In this sense, current trends focus on the promotion of onsite renewable energy sources, as well as on the electrification of heating and cooling demands and sector coupling approaches through Power-to-heat strategies. This minimizes energy transportation losses while creating an increased need for storage systems. In this scenario, Thermal Energy Storage (TES) systems gain importance and provide the required flexibility, although the experience with high storage periods and volumes, fast response capacity, easy integration into building facilities and cost-effective and environmentally friendly solutions is still scarce. This paper focuses on a TRNSYS (Transient System Simulation Program) modelling and simulation analysis of different integration strategies of a TES system based on Phase Change Materials (PCM) into a real-scale tertiary building. The target building (CARTIF III) is located in Valladolid, Spain, within a Mediterranean climatic area, and incorporates different energy systems including a local photovoltaic (PV) field and a geothermal heat pump (HP). The combination of the PCM storage with the PV and the geothermal HP is studied for the cooling season (in summer) aiming at maximizing the overall system energy efficiency and minimizing the energy import from the grid, thus pursuing a Smart Island concept. Results from this study will feed the solution design for the actual integration project that will be addressed within the framework of a upcoming EU research project.

Hydrogen-Based Networked Microgrids Planning Through Two-Stage Stochastic Programming With Mixed-Integer Conic Recourse

Networked microgrids that integrate the hydrogen fueling stations (HFSs) with the on-site renewable energy sources (RES), power-to-hydrogen (P2H) facilities, and hydrogen storage could help decarbonize the energy and transportation sectors. In this paper, to support the hydrogen-based networked microgrids planning subject to multiple uncertainties (e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles), we propose a two-stage stochastic formulation with mixed integer conic program (MICP) recourse decisions. Our formulation involves the holistic investment and operation modeling to optimally site and configure the microgrids with HFSs. The MICP problems appearing in the second-stage capture the nonlinear power flow of networked microgrids system with binary decisions on storage charging/discharging status and energy transactions (including the trading of electricity, hydrogen, and carbon credits to recover the capital expenditures). To handle the computational challenges associated with the stochastic program with MICP recourse, an augmented Benders decomposition algorithm (ABD) is developed. Numerical studies on 33- and 47-bus exemplary networks demonstrate the economics viability of electricity-hydrogen coordination on microgrids level, as well as the benefits of stochastic modeling. Also, our augmented algorithm significantly outperforms existing methods, e.g., the progressive hedging algorithm (PHA) and the direct use of a professional MIP solver, which has largely improved the solution quality and reduced the computation time by orders of magnitude. Note to Practitioners—This paper proposes an optimal planning model for electricity-hydrogen microgrids with the renewable hydrogen production, storage, and refueling infrastructures. Our planning model is extended under a two-stage stochastic framework to address the multi-energy-sector uncertainties, e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles. The first-stage problem is to optimize the siting and sizing plan of microgrids. Then, in the second-stage problem, the coordinated scheduling of electricity and hydrogen supply systems is modeled as second-order conic programs (SOCPs) to accurately capture the power flow representation under stochastic scenarios. Also, the logical constraints with binary variables are introduced to describe the energy transactions and storage operations, which results in an MICP recourse structure. Note that the stochastic MICP formulation could be very challenging to compute even with a moderate number of scenarios. One challenge certainly comes from integer variables that cause the problem nonconvex. Another challenge follows from the fact that the strong duality of SOCPs might not hold in general. To mitigate those two challenges, we prove that the continuous relaxation of our recourse problem has strong duality, and make use of that continuous relaxation and other enhancements to design an augmented decomposition algorithm. As revealed by our numerical tests, the proposed decomposition method outperforms PHA in both the solution quality and computational efficiency. Comparing to the PHA, our ABD method often achieves tighter bounds with trivial optimality gaps. Also, it could reduce the computation time by orders of magnitude. With the help of advanced analytical tool, the proposed planning framework can be readily implemented in real-world applications.

Energy management of Internet data centers in multiple local energy markets

Active distribution network Internet Data Centers (IDCs) are large energy consumers in modern distribution networks. The integration of renewable energy and energy storage systems transforms IDCs into proactive energy prosumers, and enables them to participate in the energy trading in regional local energy markets. This paper proposes an energy management framework for a Cloud Service Provider (CSP) that manages multiple geographically distributed IDCs. The framework provides decision-making support for the CSP to optimally schedule the IDCs’ cyber-energy resources (computing requests, on-site battery energy storage systems, and on-site renewable energy sources). In this way, the proposed framework facilitates the CSP to proactively participate in the local energy markets situated in the regions that host the IDCs, with the objective to minimize the CSP's total cost over a finite operation horizon. We use an effective evolutionary computation algorithm to solve the proposed energy management model and design extensive numerical simulations to validate the effectiveness of the proposed method.

Multi-Objective and Cooperative Power Planning for Datacenter With On-Site Renewable Energy Sources

Multi-Objective and Cooperative Power Planning for Datacenter With On-Site Renewable Energy Sources

Reliability-based energy scheduling of active buildings subject to renewable energy and demand uncertainty

The increasing penetration of renewable energy sources (RESs) and the inherent volatility in demand profiles have added another layer of complexity to the management of energy resources in modern active buildings (ABs). Yet, three challenges have been neglected in previous studies: (1) There is no universal systematic method for identifying an AB’s general stochastic model; (2) No research has been conducted on the reliability-based design optimization for ABs’ energy supply; (3) Uncertain sources are not categorized based on their importance in regard to the optimization problem. This article aims to solve these challenges by proposing a probabilistic-based optimization approach for solving the reliability issue of energy supply in buildings with on-site renewable energy sources (RESs), taking into account the uncertainty associated with photovoltaic (PV) production and demand fluctuations. The suggested framework seeks to reduce the overall costs of the system while ensuring high energy supply reliability. The proposed methodology, when applied to a real-world case study, demonstrates a 60% increase in reliability of energy supply as compared to typical deterministic methodologies.

An Assessment of Renewable Energies in a Seawater Desalination Plant with Reverse Osmosis Membranes.

The purpose of our study was to reduce the carbon footprint of seawater desalination plants that use reverse osmosis membranes by introducing on-site renewable energy sources. By using new-generation membranes with a low energy consumption and considering wind and photovoltaic energy sources, it is possible to greatly reduce the carbon footprint of reverse osmosis plants. The objective of this study was to add a renewable energy supply to a desalination plant that uses reverse osmosis technology. During the development of this research study, photovoltaic energy was discarded as a possible source of renewable energy due to the wind conditions in the area in which the reverse osmosis plant was located; hence, the installation of a wind turbine was considered to be the best option. As it was a large-capacity reverse osmosis plant, we decided to divide the entire desalination process into several stages for explanation purposes. The desalination process of the facility consists of several phases: First, the seawater capture process was performed by the intake tower. This water was then transported and stored, before going through a physical and chemical pre-treatment process, whereby the highest possible percentage of impurities and organic material was eliminated in order to prevent the plugging of the reverse osmosis modules. After carrying out the appraisals and calculating the amount of energy that the plant consumed, we determined that 15% of the plant’s energy supply should be renewable, corresponding to 1194 MWh/year. As there was already a wind power installation in the area, we decided to use one of the wind turbines that had already been installed—specifically, an Ecotecnia turbine (20–150) that produced an energy of 1920 MWh /year. This meant that only a single wind turbine was required for this project.

On-site renewable energy for industrial buildings in Sri Lanka: a life-cycle cost analysis

ABSTRACT Industrial sector consumes a substantial portion of electrical energy. Building owners persistently believe that combining on-site renewable energy systems to buildings is not cost-effective in Sri Lanka. Therefore, this research aimed to evaluate the use of on-site renewable energy sources for industrial buildings in Sri Lanka. This research carried out 10 expert interviews and 6 case studies. Experts determined that solar and biomass as viable on-site energy generating sources in Sri Lanka. Life-cycle cost was calculated for six case studies representing three case studies for each viable option followed by a sensitivity analysis. Based on the research findings, biomass can effectively manage waste while eliminating the risk of the emission of harmful biomethane. Based on the calculations, the payback period for a solar system varies between 4 and 6 years and biomass system varies between 6 and 7 years. This research explored the potential of generating on-site renewable energy for industrial building. Although the ‘on-site’ energy generation is not widely discussed in industrial buildings, this research study strengthened the possibility of adopting solar and biomass as on-site renewable energy generation sources.