On-site Photovoltaic Research Articles

How Not to Reduce Carbon Dioxide Emissions: An Unbalanced Focus on Energy Efficiency in Germany’s Building Rehabilitation Policies

Germany needs to reduce CO2 emissions from space heating in its old buildings to net zero by 2045 to fulfil its climate goals. However, direct CO2 reduction measures in existing buildings receive relatively little subsidy support from the federal government’s German Development Bank, compared to generous subsidies for energy efficiency measures. This interdisciplinary paper evaluates this phenomenon by comparing costs and CO2 abatement effects of ever higher energy efficiency measures, alongside the costs of direct CO2 reduction through heat pumps and onsite photovoltaics. It uses a set of carefully selected reports on the costs and benefits of renovation to a range of energy efficiency standards in three common types of multi-apartment buildings in Germany, updating these for 2024 construction, energy, and finance costs. The cost of the CO2 saved is extremely high with energy efficiency measures and absurdly high with the highest energy efficiency standards, up to 20 times the cost of CO2 abatement through other means, such as offsite renewables. This reduces markedly with onsite CO2 reduction measures. This paper sets this analysis in the context of asking what social, cultural, and discursive factors extol energy efficiency so highly that policy tends to thwart its own stated goal of deeply reducing CO2 emissions.

Integrating virtual power plants for sustainable supply chain operations in production-climate nexus

ABSTRACT This paper aims to design and integrate virtual power plant (VPP) into a geographically dispersed production-distribution network for attaining environmental sustainability goal. The VPP is comprised wind turbines, photovoltaics, combined heat and power, and energy storage units. The proposed VPP-based supply chain design model makes strategic allocations on renewable capacity as well as scheduling production, inventory, transportation, and energy generation and trading decisions. The model represents a first-of-its-kind in planning a multi-layer supply chain network in which renewable-based electric-thermal cogeneration is coordinated in production-climate nexus. The study yields the following findings. First, products with larger energy use opt to be made in locations with stronger sunshine or wind profile despite higher transportation costs. Second, the time-of-use rate greatly stimulates the installation of onsite photovoltaics and energy storage. Third, under flat utility rate, reducing energy storage cost has limited benefit to the cost savings of the supply chain operations.

Large-scale EV charging scheduling considering on-site PV generation by combining an aggregated model and sorting-based methods

Large-scale electric vehicle (EV) charging scheduling is highly relevant for the growing number of EVs, while it can be complex to solve. A few existing studies have applied a two-stage scheduling approach to reduce computation time. The first stage approximates the optimal overall load, and the second prioritizes the charging. This work also attempts to apply such an approach for large-scale EV charging considering on-site photovoltaic (PV) generation at a workplace. However, validation and analysis are missing to address whether and why the two-stage approach is suitable. Besides, the existing studies lack exploring different methods to prioritize charging. This work investigates the two-stage approach. Simulation results show the non-uniqueness of the optimal solution from the optimal individual model, and guided by the optimal overall load, sorting-based methods can often lead to an optimal solution, while non-optimal solutions only cause decreases in the load-matching performance with a median value of less than 1%. The aggregated model usually cannot achieve the optimal overall load due to model simplifications. However, further applying sorting-based methods will reduce the differences between the final and the optimal overall load. Thus, the two-stage approach is suitable for this study, and further simulations show that it can achieve almost the optimal annual performance with around 1/57 of the computation time. Furthermore, this study explores different methods to prioritize charging. Simulation results show no difference in performance, while the Least Laxity First method leads to around 54.6% more switching.

Flexible coupling and grid-responsive scheduling assessments of distributed energy resources within existing zero energy houses

Technological advancement and supportive government policy have accelerated investments in distributed energy resources (DERs) in buildings, demand-side management has become essential to fully exploit their value, including demand flexibility and grid support. Using high-resolution smart-meter data, this work presents an optimization framework for assessing the flexible scheduling of coupling distributed resources in existing zero energy houses (ZEHs) in Kyushu, Japan. Two price-based energy management programs are investigated for different energy coupling models with associated variables and constraints. Specifically, demand sides are encouraged to adjust energy usage and grid feed-in photovoltaics (PV) generation following a fixed Feed-in Tariff (FiT) value or market-oriented Feed-in Premium (FiP). The scheduling results present the impacts of proposed dispatch strategies on PV consumption and grid interactions. Results show two price-following strategies result in significant differences in dynamic grid feed-in power flows, the trade-off between dynamic PV self-consumption and grid import helps reduce the annual energy cost to feed PV generation by matching real-world spot prices. The improvement of PV self-consumption through coupling strategies has a close relationship with the pattern of household demand profiles. The energy flexibility potential of the heat pump system is constrained by daily hot water demand, flexible scheduling of coupled PV/battery/heat pump systems achieves the highest PV self-consumption ratios. The flexible dispatch of battery storage systems presents reliable performances in increasing on-site PV self-consumption and reducing annual household energy costs, the effects of storage charging rates on cost savings are less, especially for larger capacity batteries. Findings may help design sustainable and equitable pathways for decentralized solar PV system deployment.

Flexibility implications of optimal PV design: building vs. community scale

The Swiss Energy Strategy 2050 aims for a significant increase in renewable energy generation, with photovoltaic (PV) energy having the largest share. Such development challenges the balance between electricity supply and demand, which can be partly mitigated through optimized PV integration together with energy storage systems. This study investigates the optimal placement of rooftop and façade PV for a small energy community considering investment and operational costs, embodied and operational emissions, and financial and environmental benefits of excess on-site electricity production, as well as batteries and different heating system options. Additionally, a methodology is developed to quantify energy flexibility needs at both the building and community scale. A case study neighborhood, comprising three single-family and two multi-family homes is optimized across different scenarios. Results show that when individual self-consumption is considered, installing an air-to-water heat pump system with a heating buffer tank allows for smaller PV systems with better grid interaction conditions. When collective self-consumption is considered, an overall reduction in PV system size is observed, contributing to reduced energy flexibility needs by effectively lowering peak feed-in power and improving the match between on-site PV production and demand.

Solar energy harvesting from the photovoltaic/thermal (PV/T) pavement: Energy performance analyses and comparison considering ground influence

Under the carbon neutrality targets, the easy-installation low-investment photovoltaic (PV) technology is vital, while the limited urban rooftop area and high building façade cost limit the onsite PV installation. Thus, harvesting solar energy from the road, namely pavement-integrated photovoltaic/thermal (PIPV(T)) technology, is promising. The 2D finite element PIPV(T) models are proposed and verified through the field test within 3% (cell temperature) and 7% (PV output) mean absolute percentage error. The parametric analyses on system designs (water tank volume, water velocity, water inlet temperature), weather conditions (solar irradiance, wind velocity), and ground boundary condition influence are conducted. The water inlet temperature acts as a minor factor, while tank volume and fluid velocity are crucial, i.e., a 32.76% thermal efficiency increase for a 125 L tank volume increase. Results show that over 0.2 L/s water flow rate is recommended for a 100 L water tank, and the solar irradiance increase adds to the thermal efficiency to 41.47% under 1000 W/m2 with 11.38% ground influence. The annual system performances for southern and northern metropolises are compared, with water tank variation considered. Results show that a small water tank leads to high temperature and low system thermal efficiency at 9.3% and 12.17% in Hong Kong and Beijing.

Performance sensitivity of hot and cold thermal storage with onsite photovoltaics and heat pumps

Heat pumps integrated with thermal storage can help in the electrification of buildings’ thermal loads while simultaneously providing flexibility to the electricity network. In this study, we investigate the energy performance of these electrified thermal systems when combined with onsite photovoltaics for a range of buildings with different thermal characteristics, orientations, occupancy profiles, and solar panel directions. This sensitivity analysis demonstrates the robustness of the potential benefits of these thermal systems when installed and operated under non-optimal conditions. The findings of this study reveal significant grid electricity savings achieved by these integrated systems, ranging from approximately 50% to 80% in both heating- and cooling-dominated regions with diverse climates, even under reasonable extreme conditions. These outcomes remain consistent regardless of variations in PV direction, house thermal performance, building orientation, and occupancy profile. By utilising thermal energy storage to store surplus solar energy, the system enhances the PV self-consumption ratio and solar fraction by approximately 30% irrespective of moderate deviations in PV direction.

On-site solar PV generation and use: Self-consumption and self-sufficiency

As energy storage systems are typically not installed with residential solar photovoltaic (PV) systems, any “excess” solar energy exceeding the house load remains unharvested or is exported to the grid. This paper introduces an approach towards a system design for improved PV self-consumption and self-sufficiency. As a result, a polyvalent heat pump, offering heating, cooling and domestic hot water, is considered alongside water storage tanks and batteries. Our method of system analysis begins with annual hourly thermal loads for heating and cooling a typical Australian house in Geelong, Victoria. These hourly heating and cooling loads are determined using Transient System Simulation (TRNSYS) software. The house’s annual hourly electricity consumption is analysed using smart meter data downloaded from the power supplier and PV generation data measured with a PV system controller. The results reveal that the proposed system could increase PV self-consumption and self-sufficiency to 41.96% and 86.34%, respectively, resulting in the annual imported energy being reduced by about 74%. The paper also provides sensitivity analyses for the hot and cold storage tank sizes, the coefficient of performance of the heat pump, solar PV and battery sizes. After establishing the limits of thermal storage size, a significant impact on self-efficiency can be realised through battery storage. This study demonstrates the feasibility of using a polyvalent heat pump together with water storage tanks and, ultimately, batteries to increase PV self-consumption and self-sufficiency. Future work will concentrate on determining a best-fit approach to system sizing embedded within the TRNSYS simulation tool.

Technical feasibility of powering U.S. manufacturing with rooftop solar PV

The use of renewable electricity is vital for the decarbonization of industry. Industrial firms source renewables through off-site power purchase agreements or on-site installations, though the latter currently supplies <0.1% of industrial electricity demand in the U.S. Manufacturing buildings typically have large, flat rooftops that are ideal for solar photovoltaic (PV) arrays. This study investigates the feasibility of using rooftop solar PV to cover the net annual electricity needs of industry across all U.S. states and manufacturing sectors. Modeled electricity supply intensity for solar PV arrays is compared with the electricity demand per unit of floorspace for average manufacturing buildings derived from the U.S. Department of Energy Manufacturing Energy Consumption Survey. Results show that, depending on geographical location, rooftop solar PV can completely fulfill the electricity requirements of 5%–35% of manufacturing sectors considered on a net annual basis (assuming net metering). Furniture, textiles, and apparel manufacturing can be powered through on-site means in nearly every location, representing 2% of U.S. manufacturing electricity use and 6% of floorspace. Considering seasonal potential during summer months expands the list of feasible sites, particularly in the U.S. Southwest. Compared to off-site sourcing of renewable energy, pursuing on-site PV installations can also enable manufacturers to maintain limited operations during periods of grid disruption, especially when coupled with on-site energy storage. Overall, the results indicate a substantial physical opportunity for industrial firms to expand rooftop solar PV from currently low levels to help meet decarbonization goals.

Electrification of residential heating, cooling and hot water: Load smoothing using onsite photovoltaics, heat pump and thermal batteries

Due to the pressing timeframe to meet emissions reduction targets, electrification of heat is the fastest approach for decarbonising buildings. However, the high power demand of electric heating and cooling devices poses a significant challenge to the electricity supply chain when they are deployed on mass. Adding rooftop solar photovoltaic systems does not necessarily eliminate this problem. Cheap and safe thermal storage offers a potentially viable solution allowing electric HVAC & hot water systems to be deployed with minimal impact on the grid. We propose such a multi-function thermal system combining solar photovoltaics, heat pump, and thermal storage for supplying residential heating, cooling and hot water demand. We investigate the effectiveness of this system in terms of electricity demand management and solar utilisation in different geographical locations. The results illustrate that with effective control, such a system can reduce a building’s annual grid-electricity demand by about 50% to 80%, and increase its solar self-consumption to around 60%. The temporal load profile is also significantly reduced and flattened, allowing high level of distributed PV penetration without negative impacts on the grid. We show that these benefits can be earned at a reasonable cost to consumers helping them to reap a positive rate of return compared to alternative options for different climatic and regional conditions. The proposed thermal storage system is demonstrated as a cost-effective and safe distributed energy solution to ease the stress exerted on the grid from excess PV power as well as heating and cooling related peak demand.

Risk-Averse Cooperative Operation of PV and Hydrogen Systems in Active Distribution Networks

Environmental emissions and decreasing costs of renewable energy sources (i.e., photovoltaic (PV), wind, hydrogen (H2), etc.) are prompting a sharp increase in renewable energy penetration into the grid. Among various energy sources, H2 energy with its promising technical, economic, and environmental merits has demonstrated great potential for large deployment in distribution systems. This article proposes a multiobjective network-constrained framework for the day-ahead scheduling of hydrogen systems (HS), including hydrogen production from water electrolysis by electrolyzers, hydrogen storage, stationary fuel cells, and fueling of fuel cell electric vehicles (FCEVs). This framework includes various physical constraints to ensure reliable operation and considers integrated demand response and conservation voltage reduction, and reactive power support from HSs for realistic day-ahead scheduling. It also incorporates the cooperative operation of PV units and HSs to supply power for water electrolysis from stationary HSs equipped with onsite PVs. Moreover, conditional value-at-risk is applied to address the risk of PV output, FCEVs’ H2 demand, loads, and market price. The proposed model is formulated as a mixed-integer linear programming problem and validated by testing on a 33-node distribution test feeder.

Hierarchical Operation Management of Electric Vehicles for Depots With PV On-Site Generation

Solar photovoltaic (PV) generation is a promising energy cost-saving selection for depots that use electric vehicles (EVs) to deliver goods; however, the intermittency of power output renders it challenging to realize reliable and economical operations. This paper proposes a two-stage hierarchical operation management method of EVs for depots with on-site PV generation. In the day-ahead stage, considering real road networks and battery swapping mode, a coordinated scheduling model of delivery service and charging/discharging for EVs is constructed to minimize the overall energy cost and logistics delivery cost by using the forecasted PV power output. Moreover, an effective solution approach based on the natural aggregation algorithm and Floyd algorithm is developed for the model. In the actual operation stage, a model prediction control (MPC)-based rolling horizon operation strategy is proposed to correct EVs’ charging/discharging decisions with the realization of real-time PV power output, aiming at to minimize the deviation in the actual and day-ahead scheduled interactive power between the depot and upper grid. Finally, simulations are conducted to validate the proposed methods.

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

Operational performance and grid-support assessment of distributed flexibility practices among residential prosumers under high PV penetration

This study explores the impacts of increased photovoltaic (PV) penetration on a real-world power grid. Detailed operational scenarios of existing distributed energy technologies, including grid-tied PV, heat pump, micro-cogeneration, and battery systems among smart residential prosumers are examined using smart meter data. Then, to quantify the grid-support interactions of the decentralized electricity generation and consumption under high grid PV penetration, we introduce grid-support indicators to recognize their induced month-to-month variations based on the dynamic grid spot trading price and carbon emission density profiles. The results show that increased electricity consumption is attractive when the flexible consumption is shifted to a low grid spot trading price and carbon emission intensity period. The grid-support benefits of distributed generators are improved when their output enables a reduction in imported electricity during the grid residual load peak period. The direct simple integration of distributed PV generation makes it difficult to achieve grid-support operation at high solar penetration levels. The analysis results provide insight into the favorable scheduling of the heat pump consumption for time periods corresponding to a large quantity of available on-site PV generation to improve the grid-support performance. The results would help policymakers govern grid-support assets more equitably from a system-level perspective.

Impact on Renewable Design Requirements of Net-Zero Carbon Buildings under Potential Future Climate Scenarios

This paper presents an analysis to foresee renewable design requirement changes of net- zero carbon buildings (NZCBs) under different scenarios of potential future climate scenarios in the U.S. Northeast and Midwest regions. A climate change model is developed in this study using the Gaussian random distribution method with monthly temperature changes over the whole Northeast and Midwest regions, which are predicted based on a high greenhouse gas (GHG) emission scenario (i.e., the representative concentration pathways (RCP) 8.5). To reflect the adoption of NZCBs potential in future, this study also considers two representative future climate scenarios in the 2050s and 2080s of climate change years in the U.S. Northeast and Midwest regions. An office prototype building model integrates with an on-site photovoltaics (PV) power generation system to evaluate NZCB performance under the climate change scenarios with an assumption of a net-metering electricity purchase agreement. Appropriate capacities of the on-site PV system needed to reach NZCB balances are determined based on the building energy consumption impacted by the simulated climate scenarios. Results from this study demonstrated the emission by electricity consumption increases as moving toward the future scenarios of up to about 25 tons of CO2-eq (i.e., about 14% of the total CO2-eq produced by the electricity energy source) and the PV installation capacity to offset the emission account for the electricity consumption increases significantly up to about 40 kWp (i.e., up to more than 10% of total PV installation capacities) as the different climate scenarios are applied. It is concluded that the cooling energy consumption of office building models would significantly impact GHG emission as future climate scenarios are considered. Consequently, designers of NZCBs should consider high performance cooling energy systems in their designs to reduce the renewable energy generation system capacity to achieve net-zero carbon emission goals.

Feasibility of applying Illinois Solar for All (ILSFA) to the Bloomington Normal Water Reclamation District

This research study assesses the feasibility of solar energy at the Bloomington Normal Water Reclamation District (BNWRD) in Bloomington, IL through examining the framework of the Illinois Solar for All (ISFA) program. ISFA is a state funded energy finance program dedicated to promoting and proliferating solar energy throughout Illinois for low-to-moderate income residents, non-profits, public facilities, and environmental justice communities. The BNWRD is a wastewater treatment plant that resides in an environmental justice community located in west Bloomington. After acquiring energy consumption data and site information from this local government facility, we incorporated the collected data set into solar photovoltaic (PV) system performance modeling tools to estimate the total electricity generated by a potential on-site solar PV system. Potential financing of the solar array according to the various financial structures established by the ISFA is also simulated with these solar energy performance models. The outcomes demonstrate potential energy savings the ISFA program can provide for the BNWRD. This research project suggests an optimum solar array system and financing plan to be used by the BNWRD through the ILSFA program, which can be a replicable model for other areas in the state. Potential vendors and facilities in Illinois intending to utilize the ILSFA program can reference this research to seek a better understanding of the functionality of the program.

Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability

In the last few decades, there has been a strong trend towards integrating renewable-based distributed generation systems into the power grid, and advanced management strategies have been developed in order to provide a reliable, resilient, economic, and sustainable operation. Moreover, demand response (DR) programs, by taking the advantage of flexible loads’ energy reduction capabilities, have presented as a promising solution considering reliability issues. Therefore, the impacts of combined system architecture with on-site photovoltaic (PV) generation units and residential demand reduction strategies were taken into consideration on distribution system reliability indices in this study. The load model of this study was created by using load data of the distribution feeder provided by Bosphorus Electric Distribution Corporation (BEDAS). Additionally, the reliability parameters of the feeder components were determined based on these provided data. The calculated load point and feeder side indicators were analyzed comprehensively from technical and economic perspectives. In order to validate the effectiveness of the proposed structure, four case studies were carried out in both DigSILENT PowerFactory and MATLAB environments.

Optimal scheduling of gas and electricity consumption in a smart home with a hybrid gas boiler and electric heating system

Space and water heating are two major components of household energy consumption, and a number of work has been done to combine multiple residential heating technologies to develop hybrid heating solutions that are more flexible, efficient and economical. This paper studied and modelled a smart home with a novel hybrid heating system, which consists of a wall-hung gas boiler (for both space and water heating) that is connected to a water tank with an auxiliary electric resistance heater (for water heating), and auxiliary split air conditioners (for space heating). A multi-objective optimization was further formulated to optimally schedule the gas and electricity consumption of a smart home equipped with the hybrid heating system as well as an onsite photovoltaic (PV) system, a battery energy storage system, an electric vehicle and various types of flexile loads. Two objectives, i.e. the minimization of operating energy cost (including both electricity and gas payment) and the maximization of thermal comfort, were considered. The optimization problem was finally transformed to a single objective mixed integer linear programming problem that can be solved by existing optimization solvers. Simulation results verified the operational performance of the studied hybrid heating system and the effectiveness of the proposed scheduling method. Sensitivity analysis was conducted considering various levels of electricity/gas prices, onsite PV capacity, battery capacity and outdoor air temperature, and it shows that in all the scenarios the operating energy cost of the smart home with the hybrid heating system was always lower than that with the pure electricity or pure gas heating systems. The performance under various uncertainties and the benefits of adopting model predictive control were also demonstrated. It is noted that this paper only focuses on the optimal scheduling and operation costs of the hybrid heating system. Optimal configuration and sizing of the heating system with the initial investment (i.e. capital expenditures) considered are identified as research directions in the future.

A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid

The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

Net-zero energy building design and life-cycle cost analysis with air-source variable refrigerant flow and distributed photovoltaic systems

Net-zero energy building (NZEB) design has been gaining widespread acceptance as a major driver to reach feasible long-term goals of energy reductions in a building sector for the coming decades. Accordingly, net-zero energy targets for commercial buildings have been specified by federal and/or local government agencies in U.S. Although moving toward NZEB goals has technically feasible long-term advantages, economic issues are one of primarily concerns by building owners and designers for the application of advanced building technologies in NZEB design of their retrofit or new building construction. However, the lack of life cycle cost (LCC) data has still hindered reliable understanding of adopting NZEB design by building owners in a cost-effective manner. In response to this gap, this study conducts life-cycle cost analysis (LCCA) of NZEB design with an energy-efficient heating, ventilation, and air conditioning (HVAC) system and a solar power generation system. To enable NZEB design, an air-source VRF heat pump (HP) type system is considered for energy-efficient HVAC equipment in an office building model with a grid-tied photovoltaic (PV) system. Results reveal that the LCC of NZEBs varies primarily due to a combined effect of annual site EUIs, on-site power generation, and cost-items of each location in U.S. climates. Some hot and mild climate zones in the U.S. represent that LCC values of NZEBs are considerably lower. In contrast, some other cold zones result in unattractive LCC values for NZEBs, caused by a combined effect of relatively lower potential of on-site PV electricity generation and city cost indices. Yet, with subsidized financing by the federal government for PV installation costs, LCC values of NZEBs become significantly attractive in most climate locations.