Photovoltaic Cost Research Articles

An agent-based modeling approach for simulating solar PV adoption: A case study of Irish dairy farms

The agricultural sector faces increasing pressure to enhance energy efficiency in light of escalating electricity costs. This study aims to simulate the adoption of photovoltaic (PV) systems in Ireland’s dairy sector using an agent-based modeling (ABM) approach to facilitate PV uptake among dairy farmers. The model incorporates factors such as grid energy costs, annual electricity consumption, annual solar generation, PV cost, and maintenance expenses to predict PV adoption likelihood. Findings reveal that by 2022, about 2.41% of Irish dairy farmers had adopted PV systems, a figure only 0.41 pp higher than the actual observed rate, validating the ABM’s accuracy. Additionally, the research forecasts future adoption rates of PV systems among dairy farmers, demonstrating the efficacy of ABM in understanding and predicting renewable energy uptake in the dairy sector. These insights can inform policy suggestions to promote renewable energy adoption, ultimately enhancing energy efficiency and sustainability in dairy sector.

A hierarchical frequency stability control strategy for distributed energy resources based on ADMM

With the large-scale distributed energy resources (DERs) interfaced by power electronic converters connected to the power grid, the traditional synchronous generation is gradually replaced, and the inertia and primary frequency regulation capability of the power system is weakened, which seriously threatens the frequency security of the power grid. In view of the above problems, a hierarchical control strategy by coordinating the distributed wind turbine (WT) and photovoltaic (PV) is presented to improve the transient frequency (TF) and steady-state frequency (SSF). For the system control level, the optimal frequency regulation coefficients of multiple WTs and PVs clustering systems are determined by the optimal control principle with minimum frequency regulation energy, while considering the constraints of TF and SSF. For the WT/PV control level, the frequency regulation coefficients of WTs and PVs are solved by the model predictive controller (MPC) and alternating direction method of multipliers (ADMM), with the minimum control cost of WT and PV. It achieves the optimal allocation of frequency regulation power and improves the computing efficiency. Finally, the validity of the proposed method is tested in the modified IEEE 10-machine 39-bus system.

Water, energy, and food nexus in a solar-powered brackish water desalination plant in Jordan

Abstract The growing demand for water in domestic, agricultural, and energy production applications poses a significant challenge for Jordan. This work assesses the role of brackish water desalination as an alternative to alleviate water scarcity in semi-arid regions. Desalination is still limited in its application in Jordan due to high electricity tariffs. Shifting to renewable sources such as solar energy, abundant in the country, is a feasible way to power technologies with a high energy demand. In this work, we study the brackish water desalination plant at the Hashemite University in Jordan that is powered by a photovoltaic (PV) solar system (the HU PV-BWRO). The plant’s performance was evaluated in the context of the water-energy nexus as a hybrid water supply solution. While this work integrates essential elements, such as water availability, technical options, economic viability, and agricultural management, the analysis primarily focuses on the technical and economic aspects related to water, energy, and food. Water assessment results indicate that the groundwater wells near the HU campus are at risk of quality degradation over time, as they have shown a slight increasing trend in salinization from 2015 to 2023. Energy assessment results show a promising performance from the HU PV-BWRO desalination plant, with a specific energy consumption (SEC) value of 1.2 kWh m−3 (140% to 400% less energy consumption compared with other Jordanian desalination plants of similar capacity). Unit price comparisons indicate that the energy cost of PV (0.042 USD/m3) is 5 times less than the cost of grid electricity (0.24 USD/m3). The operational cost of the solar desalination plant at full capacity, is USD 0.23/m3. This is about 260% less than the operation cost for local, grid-powered desalination plants. Finally, it is estimated that by operating the plant at 50% of its total capacity, the produced water could be sufficient to irrigate up to 80% of the HU campus to increase agricultural production. This study highlights the importance of decreasing reliance on energy for water and food production, and it shows that the use of solar powered desalination could be used as an example in semi-arid regions, particularly in terms of integrating renewable energy and energy efficiency.

Optimizing quasi-dynamic wireless charging for urban electric buses: A two-scenario mathematical framework with grid and PV-battery systems

This paper presents a mathematical optimization framework for the strategic placement of quasi-dynamic wireless charging (QWC) stations within road networks to address the charging needs of battery electric buses (BEBs). This study evaluates two scenarios for powering the buses. In the first scenario, a grid-connected system is considered. The optimization aims to minimize annual costs related to capital, operation, and energy losses of the electric bus fleet. This involves determining the optimal locations for QWC stations, the length of power transmitters, and the corresponding battery capacities for the BEBs. Using MATLAB-based optimization tools Casadi and Yalmip, with solvers Bonmin and Fmincon, the optimal configuration includes a 13 kWh battery capacity and a 300 m power transmitter distributed across five bus stop areas. The second scenario employs a chance-constrained optimization approach for an isolated solar photovoltaic (PV) and battery energy storage system (BESS). This system is designed to reliably meet the BEBs' energy requirements throughout the day, considering different seasonal data (winter, summer, all seasons/year-round). The optimization results for the PV and BESS capacities vary with the seasons: 394.247 kW and 2012.6 kWh using summer data, 1762.1 kW and 2738.2 kWh using winter data, and 1610.8 kW and 2741.9 kWh using year-round data. Additionally, the paper examines the impact of varying bus fleet sizes on the optimal battery size and power transmitter combination using a real-world example of the bus route between Khalifa City and Abu Dhabi Downtown in the UAE. The findings suggest that larger batteries with fewer or no charging stations are more economical for smaller fleets. Conversely, as the fleet size increases, a combination of smaller battery sizes and a greater number (and length) of QWC (power transmitters) becomes more cost-effective. This research offers significant insights into the efficient deployment of QWC stations and the integration of renewable energy and energy storage for sustainable urban electric bus networks. The proposed optimization models provide a systematic approach to designing and operating charging infrastructure, contributing to sustainable urban transportation systems. Moreover, the study highlights the influence of seasonal data on PV system sizing and costs.

A System Dynamics Model for Rooftop Solar PV System Development in Indonesia

Despite of Indonesia’s vast solar energy potential according to the Indonesian Ministry of Energy and Mineral Resources, Indonesia’s total installed capacity of rooftop solar photovoltaic (PV) is very far from Indonesia’s target of 3.6 GW in 2025. Indonesia once applied net metering scheme for rooftop solar PV policy and was expected to be able to boost rooftop solar PV growth. A system dynamics approach is used in the research to develop an assessment model to evaluate the policies’ impact on residential rooftop solar PV system growth. A Causal Loop Diagram was established then transformed into Stock and Flow Diagram (SFD) using software Vensim PLE 10.1.3, which was used to simulate several policies’ scenarios related to residential rooftop solar PV adoption and CO2 emissions reduction. Ten scenarios were simulated in this study involving three groups of intervention: initial net metering tariff, reduction on initial solar PV cost, upper limit of ROI, and combination of initial net metering tariff and initial solar PV cost reduction. The simulations revealed that combination of increasing net metering tariff to 80% & initial cost reduction 30% has the highest potential solar PV installations, the highest CO2 emissions reduction, and the lowest accumulation cost of net metering in 2030. This study can be used as reference by the policy makers in Indonesia to formulate the optimum policy to boost rooftop solar PV growth as the simulations shows that residential rooftop solar PV with the right intervention can meet the government’s target of rooftop solar PV in 2030.

Optimizing Cost and Emission Reduction in Photovoltaic–Battery‐Energy‐Storage‐System‐Integrated Electric Vehicle Charging Stations: An Efficient Hybrid Approach

In this article, an optimal photovoltaic (PV) and battery energy storage system with hybrid approach design for electric vehicle charging stations (EVCS) is proposed. The hybrid approach combines the use of polar transformer networks (PTNs) and the puzzle optimization algorithm (POA); hence it is called as POA–PTN approach. The main objective is to lessen the charging station cost and pollutant emissions. The proposed method is minimizing the pollutant emissions and the annual cost of PV with EVCS, which is done by POA method and predicts the optimal solution is done by PTN approach. The proposed method is executed in the MATLAB platform, and compared with existing methods, including heap‐based optimization, particle swarm optimization, and wild horse optimizer approaches. The proposed technique demonstrates superior performance, achieving the lowest net present cost of €1.45 × 106and emissions of 2.25 × 108 g year−1with a shorter computing time of 13.04 s compared to the other methods.

Cost accounting and economic competitiveness evaluation of photovoltaic power generation in China —— based on the system levelized cost of electricity

Accelerating the penetration of photovoltaics (PV) oriented renewables is a vital mainstay in climate mitigation. Along with continuous growth of PV generation in the power system, PV costs have been rapidly declining. Levelized cost of electricity (LCOE) is commonly applied to cost accounting of energy, while neglecting the specific cost compositions of PV leads to an overly optimistic scenario. By integrating grid costs and balancing costs into conventional LCOE framework, a System LCOE (S-LCOE) model was constructed to evaluate the economic feasibility of PV generation, more accurately. The results revealed that all provincial S-LCOE of China's PV is currently higher than local desulfurized coal electricity price (DCEP). Based on the comparative analysis of provincial S-LCOE and DCEP, four regions with diverse economic competitiveness were identified. PV projects in Region I and Region II are considered to be potentially competitive comparing to thermal generation, in terms of environmental benefits and S-LCOE, especially in Guangdong, Jilin, and Hainan. Furthermore, by simulating the descent trajectory of S-LCOE, we estimate that China will achieve S-LCOE equal to DCEP in Region I as early as 2023, while it will be postponed to 2042 in Region Ⅳ at the latest, depending on different S-LCOE descent rates.

Techno-economic optimization of hybrid energy systems for zero energy buildings in remote communities: a case study from Turkey

This study evaluates the economic efficiency and viability of optimizing hybrid renewable energy systems (HRES) for zero-energy buildings (ZEBs) in remote communities, with a specific focus on Ankara, Turkey, in response to the increasing demand for renewable energy driven by concerns over fossil fuel scarcity, environmental sustainability, and rising conventional energy costs. Using the Hybrid Optimization Model for Multiple Energy Resources (HOMER) program, known for its advanced algorithms that accurately model and optimize hybrid systems by considering factors such as weather data, load profiles, and equipment specifications, we perform a comprehensive techno-economic analysis. We explore five different HRES configurations, combining photovoltaic (PV) panels, wind turbines (WT), diesel generators (DG), and battery storage systems, to determine the most cost-effective and reliable solution for powering approximately 30 rural households. The analysis reveals that the optimal configuration includes 107 kW of PV, three 10 kW WT, a 10 kW DG, and 45 units of 7.15 kWh batteries, demonstrating a net present cost (NPC) of $568,431 and a cost of energy (COE) of $0.257/kWh. This setup achieves significant annual energy production of 165,068 kWh from PV, 96,329 kWh from WT, and 27,100 kWh from DG. This configuration maintains a high state of charge (SoC) in the battery storage, ensuring system stability and extending the battery lifespan. The system's ability to consistently meet load demands with minimal reliance on the DG highlights its superior techno-economic synergy compared to other scenarios. Sensitivity analysis reveals that a doubling of fuel prices increases COE by 14% and NPC by 13%, while a 40% reduction in PV and WT capital costs decreases COE and NPC by approximately 16% and 18%, respectively. Furthermore, declining expenses associated with PV and WT installations emphasize the ongoing affordability of renewable energy solutions. These results provide valuable insights for the deployment of cost-effective and reliable HRES in similar remote locations, contributing to the broader goal of sustainable energy solutions for ZEBs.

An efficient optimization framework for distribution network planning by simultaneous allocation of photovoltaic distributed generations and transformers

AbstractAmong the distribution network planning problems, allocation of transformers is one of the most important and challenging ones. On the other hand, owing to the widespread growth of distributed generations (DGs), their inclusion in the planning problems is vital. This paper proposes an efficient optimization framework for simultaneous allocation of photovoltaic (PV) systems and service transformers in the distribution network. For this aim, to improve the network performance, location and size of the transformers and PV units are optimally determined. Due to the difficulty of the planning problem, four variants of crow search algorithm (CSA) including original CSA, differential CSA (CSAd), directed differential CSA (CSAdd) and chaotic directed differential CSA (CSAcdd) are introduced and applied to the planning problem. To evaluate the impact of PV cost on the results, the planning problem is solved considering different PV system costs. Simulation results show that at the price of $1.25/W, installation of PV systems is cost‐effective, so that over the case study, 400 kW PV system was installed. By decreasing the PV price to $1.2/W, the maximum capacity considered for a PV system is installed. Moreover, on average, CSAd produces more accurate and robust results than the other studied algorithms.

A Hybrid Technique for Optimal Placement of Fast‐Charging Stations of Electric Vehicles for the Reliability of Distribution Network

Proper planning for electric vehicle (EV) charging stations, as well as the necessary network development, is critical for the continued growth of EVs and conventional loads. This article proposes a hybrid method for the allocation of fast‐charging stations (FCSs) and photovoltaic (PV) with battery energy storage (BES) and scheduling. The proposed hybrid technique is the wrapper of golden jackal optimization (GJO) and random forest algorithms (RFA), commonly named the GJO–RFA technique. Using the GJO approach, the allocation and EV assignment problems are resolved. RFA is used to address EV, traffic flow, and PV‐related uncertainties. The objective of the proposed approach is to minimize the loss of energy, investments, index of voltage deviation, and operation and maintenance costs of FCS, PV, and BES. The associated important factors are also assessed, including the number of charging ports, FCS capacity, and EV flow caught using a FCS. As a test example, a 33‐node radial distribution network with the appropriate traffic network is used. For the final problem, the proposed approach to minimal energy loss is 2622.7270 kWh. By then, the performance of the proposed approach is executed in MATLAB, and it is contrasted with other existing techniques. The result shows that the proposed method‐related energy loss is less than existing approaches.

Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

AbstractThis paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case.

Double-layer home energy management strategy for increasing PV self-consumption and cost reduction through appliances scheduling, EV, and storage

Maximizing self-consumption of the photovoltaic (PV) generation is an important factor to increase the penetration of PV in the residential grid. It can improve PV system profitability, save energy and reduce grid stress. This study proposes a double-layer home energy management strategy to increase PV self-consumption and reduce household electricity costs. The first layer involves rescheduling shiftable appliances to operate during surplus PV generation hours, while the second layer employs a multi-objective energy management strategy based on Jaya and particle swarm optimization (PSO) algorithms to optimize power exchange between the energy storage system (ESS) and electric vehicle (EV), with smart home. Six different scenarios are simulated to investigate the role of household appliances, ESS, and EV technology in increasing PV self-consumption. Results scheduling showed that the proposed strategy can increase self-consumption and reduce grid consumption by 17–41% and 27–78%, respectively, depending on the proposed scenarios. The double-layer strategy is found to be more effective than the single-layer approach, with significant economic benefits. The proposed method provides an efficient solution for improving PV system profitability, saving energy, and reducing grid stress. Thus, this study contributes to the development of sustainable energy management systems, paving the way for future research in this area.

Techno‐economic optimization of a hybrid system composed of pumped hydro storage, photovoltaic module, and diesel generator in a multi‐objective framework

AbstractHybridization of photovoltaic (PV) module (as a non‐dispatchable resource), diesel generator (as a dispatchable source), and pumped hydro storage (PHS) (as an energy storage) can provide a promising hybrid energy system (HES). The main outlook of the present study is to develop an efficient multi‐objective framework for optimal design of an off‐grid PV/diesel/PHS HES in which a wide range of parameters related to PHS system is optimized. In the optimization framework, nine decision variables (number of PV modules, number of diesel generators, reservoir installation height, reservoir depth, reservoir diameter, pump's rated power, turbine's rated power, charge pipe diameter, and discharge pipe diameter) are optimized with respect to two objective functions: total net present cost (TNPC) and loss of power supply probability (LPSP). To determine how TNPC is affected by the change of the input variables, a sensitivity analysis is conducted by varying capital cost of PV, PHS, and diesel generator. To obtain a well‐distributed and widely spread Pareto front, a multi‐objective chaotic crow search algorithm (MO‐CCSA) is introduced. Simulation results show that at LPSP = 0% and 5%, levelized cost of energy is around 0.59 and 0.55 $/kWh, respectively. Moreover, variation of the PV capital cost has a significant impact on the TNPC value.

Solar irrigation in sub-Saharan Africa: economic feasibility and development potential

Irrespective of water resource abundance, agriculture in sub-Saharan Africa (SSA) is predominantly rainfed. Along with fertilization, irrigation could support smallholder farmers with stabilizing crop yields, increasing incomes, and achieving food security. A key barrier to irrigation uptake is inadequate rural electricity supply for pumping and distributing water, besides other infrastructure deficits. Here we devise a spatially explicit integrated modelling framework to show that over one third of unmet crop water requirements of 19 major crops in smallholder cropland of SSA could be supplied with standalone solar photovoltaic (PV) irrigation systems that can be paid back by farmers within 20 years. This accounts for 60 km3 yr−1 of blue irrigation water requirements distributed over 55 million ha of currently rainfed harvested area (about 40% of the total). Crucially, we identify 10 million ha with a profit potential >$100 ha−1 yr−1. To finance such distributed small-scale infrastructure deployment and operation, we estimate an average discounted investment requirement of $3 billion yr−1, generating potential profits of over $5 billion yr−1 from increased yields to the smallholder farmers, as well as significant food security and energy access co-benefits. We demonstrate the critical importance of business models and investment incentives, crop prices, and PV & battery costs in shaping the economic feasibility and profitability of solar irrigation. Yet, we find that without strong land and water resources management infrastructure and governance, a widespread deployment of solar pumps may drive an unsustainable exploitation of water sources and reduce environmental flows. Our analysis supports public and private stakeholders seeking to target investments along the water–energy–food–economy–sustainable development nexus.

Leveraging Green Ammonia for Resilient and Cost-Competitive Islanded Electricity Generation from Hybrid Solar Photovoltaic-Wind Farms: A Case Study in South Africa.

Hybrid solar photovoltaic (PV) and wind generation in combination with green ammonia as a seasonal energy storage vector offers an excellent opportunity to decrease the levelized cost of electricity (LCOE). In this work, an analysis is performed to find the most cost-effective configuration of power-to-ammonia-to-power (P2A2P). In P2A2P, wind and solar resources are combined with energy storage to design a resilient electricity grid. For daily generation, batteries are utilized for energy storage, whereas ammonia is employed to cope with seasonal fluctuations. The costs of energy storage capacity have a significant influence on the LCOE. Therefore, this work studies the effect of solar/wind hybrid generation systems and energy storage capacity on the LCOE. A base case of the region of De Aar in South Africa was selected because this inland location has excellent wind and solar resources. The optimized battolyzer and Haber-Bosch design capacity led to an overall load factor of 20-30%. At a 30% load factor, a hybrid system with 37% wind-based and 63% solar-based energy generation capacity was the most cost-effective configuration, resulting in a LCOE of 0.15 USD/kWh at a 5% annual discount rate. In an optimistic scenario for PV costs, the LCOE achieved is essentially unaltered (0.14 USD/kWh), while the contribution of wind and PV changes to 25 and 75%, respectively. This analysis indicates that appropriate designing of hybrid energy solutions will play a key role in determining the final energy storage capacities needed to reduce the LCOE. While these costs for LCOE are above those reported for coal-powered electricity in South Africa (e.g., 0.072 USD/kWh for businesses and 0.151 USD/kWh for households), a carbon tax of 50 USD/ton of CO2 can increase these costs to 0.102 and 0.191 USD/kWh, rendering a more promising outlook for the P2A2P concept.

SolarEV City Concept for Paris

Urban decarbonization is one of the pillars for strategies to achieve carbon neutrality around the world. However, the current speed of urban decarbonization is insufficient to keep pace with efforts to achieve this goal. Rooftop photovoltaics (PVs) integrated with electric vehicles (EVs) as battery is a promising technology scalable to supply CO2-free, affordable, and dispatchable electricity in urban environments (“SolarEV City Concept”). Here, we evaluated Paris, France for the decarbonization potentials of rooftop “PV + EV” in comparison to the surrounding suburban area Ile-de-France and the reference city Kyoto, Japan. We assessed various scenarios by calculating energy sufficiency, self-consumption, self-sufficiency, cost savings, and CO2 emission reduction of the PV + EV system or PV only system. We found that above a certain roof coverage 50–60% of the total roof area for Paris or 20–30% for Ile-de-France, PV electricity production regularly exceeds the demand. Above that roof coverage, feed-in-tariffs (FIT) or storage are needed to further realize the potential of the rooftop PV system. The combination of PVs with EVs is more effective in Ile-de-France than in Paris because large surplus electricity from rooftop PV in suburban area requires large storage. With already low-carbon electricity of France by nuclear power, the maximum potential CO2 abatement through the implementation of SolarEV City Concept in Paris (0.020 kgCO2/kWh reduction from 0.063 kgCO2/kWh) is limited in comparison to that in Kyoto (0.270 kgCO2/kWh reduction from 0.352 kgCO2/kWh). However, with declining costs of PVs and EVs in the coming decades, low-cost dispatchable electricity by the PV + EV system throughout Paris and Ile-de-France may facilitate the electrification of CO2 emitting appliances, potentially allowing rapid CO2 emission reductions from Paris.

Effect of photovoltaic energy penetration on photovoltaic–wind renewable energy system

India is a tropical country that gets a significant amount of solar irradiation that is suitable for photovoltaic (PV) applications. The country is also endowed with wind energy in its large coastal areas. India is an agro-economic country that has a growing need for irrigation. Utilization of hybrid renewable energy for the agricultural needs of the country would be a step toward a sustainable future. For the environmental conditions of Haldia, India, a stand-alone PV–wind–lead-acid battery hybrid renewable energy system (HRES) was developed to cater to the needs of agricultural activities. An investigation was conducted on the impact of PV penetration on the system wind energy capacity, battery capacity, capacity shortage, net present cost, cost of energy (COE), PV and wind energy percentage and surplus energy produced. The optimization was based on the assumption that the HRES had no unmet load and the lowest COE. This research provided a range of wind energy capacities for the location with no unmet loads. The research discovered the ideal HRES of the site with a COE of US$0.312/kWh. This study may help farmers by boosting their reliance on power from renewable energy sources and decreasing their dependency on grid power for agricultural activities.

Cluster Partition-Based Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks

Considering the possible overvoltage caused by high-penetration photovoltaics (PVs) connected to the distribution networks (DNs), a cluster partition-based voltage control combined day-ahead scheduling and real-time control for distribution networks is proposed. Firstly, a community detection algorithm utilizing a coupling quality function is introduced to divide the PVs into clusters. Based on the cluster partition, day-ahead scheduling (DAS) is proposed with the objective of minimizing the operating costs of PVs, as well as the on-load tap changer (OLTC). In the real-time control, a second-order cone programming (SOCP) model-based real-time voltage control (RTVC) strategy is drawn up in each cluster to regulate the PV inverters, and this strategy can correct the day-ahead scheduling by modifications. The proposed strategy realizes the combination of day-ahead scheduling and real-time voltage control, and the optimization of voltage control can be greatly simplified. Finally, the proposed method is applied to a practical 10 kV feeder to verify its effectiveness.

Optimal capacity of solar photovoltaic and battery storage for grid‐tied houses based on energy sharing

AbstractThis paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for a grid‐connected house based on an energy‐sharing mechanism. The grid‐connected house, also mentioned as house 1 where it is relevant, shares electricity with house 2 under a mutually agreed fixed energy price. The objective is to minimize the cost of electricity (COE) for house 1 while decreasing the electricity cost of house 2. Practical factors such as real data for solar insolation, electricity consumption, grid constraint, ambient temperature, electricity rate, and battery degradation are considered based on actual data. The developed methodology is examined by taking the actual load data of two houses in South Australia. Different scenarios of contract years between the houses are investigated to make it more practical in real life. Sensitivity analyses are conducted for the sharing of energy between the houses and by changing parameters like export power limitation, load of houses, and costs of PV and BES. Likewise, operational analysis is done for two days of summer and winter. It is found that when energy sharing is applied, the optimal design of the PV‐BES system will achieve lower COE for both houses.

On the Adoption of Rooftop Photovoltaics Integrated with Electric Vehicles toward Sustainable Bangkok City, Thailand

Realizing urban energy systems with net-zero CO2 emissions by 2050 is a major goal of global societies in building sustainable and livable cities. Developing cities hold a key to meeting this goal, as they will expand rapidly in the next decades with increasing energy demand, potentially associated with rising CO2 emissions and air pollution if fossil fuels continue to be utilized. Therefore, identifying equitable, cost-effective, and deep decarbonization pathways for developing cities is essential. Here, we analyzed Bangkok City, Thailand, using the System Advisor Model (SAM) for techno-economic analysis to evaluate the decarbonization potential of rooftop photovoltaics (PV) integrated with electric vehicles (EVs) as batteries on a city scale. The analyses took into consideration hourly local weather conditions, electricity demand, electricity tariffs, feed-in-tariffs, degradation, declining costs of PV and EV, etc., specific to Bangkok. As the prices of PV and EVs decrease over the next several decades, the “PV + EV” system may provide a basis for new urban power infrastructure with high energy efficiency, low energy cost, and large CO2 emission reduction. The results show that the “PV + EV” scenario in 2030 has the highest CO2 emission reduction of 73% from electricity and vehicle usage, supplying 71% of the electricity demand of the city. The “PV + EV” system may reduce energy costs by 59% with estimated technology costs in 2030. Most of the energy generated from rooftop PV is consumed owing to large EV battery capacities, which can contribute to the rapid decarbonization of Bangkok City by 2050.