Photovoltaic Self-consumption Research Articles

Joint planning for PV-SESS-MESS in distribution network towards 100% self-consumption of PV via consensus-based ADMM

The integration of distributed photovoltaic (PV) has become a prominent characteristic of distribution network (DN). However, achieving 100% PV self-consumption poses challenges due to intermittent generation and varying load demands. To address these challenges, this paper proposes an innovative joint planning method for PV, stationary energy storage system (SESS), and mobile energy storage system (MESS) using the consensus-based alternating directional multiplier method (ADMM). First, to account for multiple scenarios with real-world variations, a two-stage clustering method with clustering by fast search and finding of density peaks and k-means is employed. Second, a joint planning model for PV, SESS, and MESS is developed to minimize the total cost of DN, including installation, operation, and maintenance costs. The model optimizes the PV capacity, SESS capacity, and MESS capacity, as well as their locations. Third, a distributed planning framework is proposed using consensus-based ADMM, which decomposes the problem into a PV planning subproblem under the worst-case scenario and energy storage system planning subproblems under multiple scenarios. Finally, the proposed method is demonstrated on an IEEE-33 node system, and the results show the method effectively enhances the access capacity of PV by 11.80% and leads to a reduction in the total cost.

Design of Three Electric Vehicle Charging Tariff Systems to Improve Photovoltaic Self-Consumption

Electric vehicles (EVs) are emerging as one of the pillars for achieving climate neutrality. They represent both a threat and an opportunity for the operation of the network. Used as flexible loads, they can favor the self-consumption of photovoltaic (PV) energy. This paper presents three EV charging tariff systems (TSs) based on the self-consumption of excess PV energy. The TS objectives are to increase the self-consumption rate (SCR) and thus indirectly decrease the charging cost of the EV users. Two of the proposed TSs correspond to an indirect control of EV charging. The third TS is a hybrid system where the charging power is controlled. The TS is designed using a series of rules that consider the momentary PV surplus and the charging power of each EV. The influence of the TS is simulated by considering real data from a PV collective self-consumption project in the Basque Country (Spain). The TS simulations performed with 6 months of data show a 13.1% increase in the SCR when applying the third TS, reaching an average of 93.09% for the SCR. In addition, the cost of EV charging is reduced by 25%.

Perception of photovoltaic energy consumption in the Spanish primary sector. An environmentally profitable alternative

Agriculture and livestock farming are activities that depend on energy consumption. Photovoltaic self-consumption systems can reduce the production costs of these actors, especially in periods of high-energy price volatility. This work aimed to determine the degree of implementation of photovoltaic self-consumption systems, their relationship with the economic impact of the energy crisis, and the perception of producers to use renewable energy sources in the Spanish agricultural system as it is one of the most important at European level. For this purpose, a survey of the Spanish agricultural and livestock system, involving 396 primary producers, was carried out between December 2022 and March 2023. The results suggest that self-consumption systems are in place in 49.1% of all farms and that these have had a positive effect in alleviating the rising energy costs suffered by the Spanish primary sector. In summary, Spanish primary producers generally have a favorable perception of the use of renewable energies on their farms, especially photovoltaic. However, the cluster analysis shows the fact that there are two types of producers, active and passive, from an environmental point of view. The characterization of this type of producers can help the Spanish Administration to improve the efficiency of its energy strategy, and can be a source of inspiration for the governments of other countries.

Profitability landscapes for competitive photovoltaic self-consumption

Photovoltaic self-consumption (PVSC) is a central element of the energy transition. However, retail electricity tariffs have not adapted to the emerging phenomenon of demand-side generation. We introduce the concept of profitability landscapes to evaluate the effects of a cost-reflective retail tariff and a competitive PVSC regulation on prosumers’ returns depending on system costs and self-consumption share. The trade-off between these two elements reveals the value of demand flexibility. We find that PVSC would be profitable in most European countries when external costs are internalized, confirm the diminishing marginal returns on demand flexibility and quantify the effects of prices, insolation and profitability on the value of demand flexibility.

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.

Techno-economic analysis on optimizing the value of photovoltaic electricity in a high-latitude location

This study performs a techno-economic analysis of different photovoltaic (PV) systems suitable for detached houses in high-latitude locations and quantifies the key economic indicators affecting their competitiveness. Residential PV systems providing different temporal power production profiles are compared, accounting for the electricity price variations in the day-ahead market and the possibility of self-consuming the produced electricity. The procedure allows novel and comprehensive case study analysis that captures PV production, self-consumption, and dynamic electricity pricing, ultimately revealing the value of the produced PV electricity. This procedure is applied to a hundred case studies representing single-family houses with different PV systems, electricity load profiles, and electricity contracts. Vertical East-West mounted bifacial panels (VBPV) reached superior performance, with 9.1% higher overall production (with 2019 weather data) and 7.4%–10.9% higher economic value (with 2019 and 2022 electricity price data) for the produced PV electricity compared with monofacial PV. Thus, VBPV is an excellent choice for households economically, but the availability of suitable installation sites in the urban environment limits the possibility of its utilization. The economic value of PV electricity strongly correlates with self-consumed electricity due to avoided transmission fees and taxes, and a significant drop in total production causes only a minor reduction in value if the amount of self-consumed electricity remains similar. Thus, for a small-scale producer who orients the panels toward the East and the West instead of the South with a 45° tilt, the economic loss is significantly smaller (even as low as 12.6%) than the production loss (23.2%).

Experimental analysis of a photovoltaic self-consumption system for the offices of a local company

The article analyzes the prospect of a solution for the mutual interaction of a photovoltaic generator and the local electrical grid, within the context of electrifying the offices of a hydrocarbon storage facility. The idea is to examine the operational performance of a system that enables the maximum self-consumption of energy generated by a small-scale photovoltaic system installed on the facility's site. Such a system could prove beneficial for multiple consumers operating in a complex energy environment. The issue is scrutinized based on experimental data from a real case in Dakar, Senegal. An installation with a photovoltaic system of approximately 17 kWp peak power is monitored for a year to assess the reliability of the self-consumption photovoltaic system for a broader application. The data encompasses both the functioning of the photovoltaic system and its interaction with the electrical grid. The potential use of this solution in the context of promoting self-consumption policies to reduce electricity consumption expenses is discussed and analyzed, revealing a relatively high level of interaction with the electrical grid. Despite maintenance costs, the photovoltaic system proves to be cost-effective.

Co-optimization of system configurations and energy scheduling of multiple community integrated energy systems to improve photovoltaic self-consumption

Given the increasing integration of building photovoltaics and the accelerated transformation of power systems, the supply-demand imbalance in photovoltaic communities has become a pressing issue. This study presents a novel optimization model for collaborative planning and scheduling. The alternating direction multiplier method is utilized to derive an optimal economic scheduling strategy for the community cooperative alliance, while ensuring private information protection. Additionally, the genetic algorithms are employed to ascertain the optimal configuration. The optimization model prioritizes the common good over individual interests in community cooperation alliances, meaning some members may need to compromise their self-interest to attain optimized overall benefits. Therefore, a compensation mechanism based on the Nash negotiation principle is established to ensure fair distribution of benefits. The simulation results demonstrate that the primary energy saving rate, annual cost saving rate, and carbon dioxide emission reduction rate of the photovoltaic community alliance are 42.91%, 32.50%, and 41.81%, respectively, and the self-consumption level of photovoltaic is vastly improved to 98.83% on a typical winter day. The sensitivity analysis illustrates that the photovoltaic panels in buildings contribute to a decrease in energy consumption and carbon emission by 25.57% and 26.15%, respectively. In contrast, they result in a cost increase of 11.05%.

A comparative study of demand-side energy management strategies for building integrated photovoltaics-battery and electric vehicles (EVs) in diversified building communities

This study compares four developed energy management strategies for a grid-connected photovoltaic-battery (PVB) system in a district energy system comprising four diverse building communities: campus, residential, office, and commercial. The proposed demand-side energy management scenarios include maximizing photovoltaic self-consumption, cost minimum strategy under time-of-use and two peer-to-peer (P2P) strategies (a Mid-Market Rate (MMR) and a novel demand response (DR) based P2P strategies). Besides, Monte Carlo integrated Markov Chain simulation method was applied to calculate the load distribution of integrated electric vehicles in diversified building communities, and P2P price was generated considering dynamic supply-demand ratio. In term of dynamic energy flow, energy cost and grid interaction, dispatch results of proposed management strategies were simulated and compared in detailed. The results demonstrate that compared to the mainstream MSC and TOU operation strategies, P2P strategies, especially the DR based P2P strategy, effectively alleviate pressure on the utility grid by actively responding to peak electricity demand and utilizing excess renewable energy generation from other communities. It is worth noting that the choice of strategy depends on the role of the building community, whether as a consumer or a prosumer. This study offers insights for decision-makers and stakeholders in managing grid-connected PVB district energy systems in diverse building communities.

Design and operational strategy optimization of a hybrid electric heating system with phase change materials for energy storage in nearly zero energy buildings

Nearly zero energy buildings (nZEBs) and the associated research on heating energy systems are gaining increasing attention. To enhance PV self-consumption capacity in nZEBs, a hybrid electric heating system with phase change materials (PCM) for energy storage using photovoltaic (PV) and grid power was developed. To study the system's performance, an experimental bench was set up, and mathematical models for energy efficiency and operational strategy were developed. Furthermore, a research building was chosen as the scenario to investigate the operational performance of the system. The analysis focused on assessing the impact of varying PV and battery capacity, specifically concentrating on load flexibility transfer capability and energy flow within the system. The results indicated that when implementing the hybrid electric heating system with varying PV and battery capacities, significant transfers of both heat and electricity loads could be achieved, leading to peak price electricity usage remaining below 10 %. Regardless of the manner in which PV is utilized, the system facilitates the transfer of both heat and electricity loads, resulting in peak price electricity usage remaining below 5 %. Through an analysis involving the optimization of system capacity and operational strategies during a simulated one-week period in the heating season, the results revealed that the utilization of a battery with a rated capacity as low as 3.69 kWh led to an increase of 10.93 kWh in PV self-consumption, resulting in a noteworthy cost reduction of 15.6 %.

Performance investigation of transitioning building services system in photovoltaic homes

Growing concern over residential carbon emissions has highlighted the necessity of transitioning conventional fossil fuel-based building services system into one that utilizes renewable energy sources. Therefore, this paper proposes a building services system, including an air-source heat pump, water storage tanks and batteries, for photovoltaic (PV) homes so that all their energy loads are electrified and mostly met by PV power. It aims to investigate the merits of the proposed system in relation to energy, economic and environmental performance by designing three distinct scenarios for power supply, space heating, space cooling, and domestic hot water (DHW). A typical Australian house equipped with a 10 kW solar PV system in Geelong, Victoria, is used as a case study. Three scenarios are modelled using Transient System Simulation (TRNSYS) software, and the simulated PV generation, space heating, and DHW load are validated using actual measured results. Findings show that the proposed system offsets all the natural gas demand of 6915 kWh required by Scenario 1 using the conventional fossil fuel-based system and increases the PV self-consumption and self-sufficiency from 18 % to 24 % in Scenario 1–38 % and 75 %, respectively. After adjusting component sizes following the principle of diminishing returns, the proposed system has a nine-year payback period and reduces carbon emissions by 67 % compared to the conventional system. This study demonstrates the capability of the proposed system to electrify our house loads, thus contributing to a decarbonized residential future.

FedPT-V2G: Security enhanced federated transformer learning for real-time V2G dispatch with non-IID data

The rising popularity of electric vehicles (EVs) underscores the potential of vehicle-to-grid (V2G) technology to contribute to load peak-shaving, valley-filling, and photovoltaic (PV) self-consumption. Effective V2G control strategies can be obtained by data-driven techniques, which is able to leverage historical and current data to inform future decision-making amidst uncertainties. However, the centralized collection and sharing of data among charging stations face challenges due to data asset concerns. Furthermore, even if data sharing hurdles are overcome, the non-independent and non-identically distributed (Non-IID) nature of data across charging stations can still negatively impact performance. In this study, we introduce FedPT-V2G, a security-enhanced federated transformer learning approach for real-time V2G dispatch that addresses Non-IID data. We employ deep learning models trained on historical and current data to enable real-time decision-making, facilitating both load shifting and PV self-consumption. Additionally, we utilize federated learning to jointly train a global model across all charging stations without collecting or sharing any local private data. We pioneer the application of the Proximal algorithm and Transformer model to tackle data distribution discrepancies within the V2G scheduling prediction task. The Proximal algorithm employs regularization techniques to align local models at each charging station more closely with the global model during updates. Concurrently, the multi-head attention mechanism within the Transformer model allows learned feature vectors to diverge, enabling better exploitation of variations across the entire feature space. Finally, we validate the proposed FedPT-V2G approach through extensive numerical simulations, demonstrating comparable performance to centralized learning on both balanced (98.93% vs 98.65%) and imbalanced (92.15% vs 92.20% in label skew) datasets.

Technical and economic analyses of PV battery systems considering two different tariff policies

Installing batteries in solar photovoltaic (PV) houses is becoming commonplace and different tariff policies give residents more options to lower their energy bills. This paper develops two rule-based control strategies to operate solar PV battery systems under fixed flat or time-of-use tariff policies, aiming to increase PV self-consumption and self-sufficiency. The battery modelling considers the charging and discharging efficiencies and the battery energy efficiency. The payback period for solar PV battery systems under the two tariff policies is also analysed considering various economic factors such as the capital cost of solar PV systems, the capital and maintenance costs of the batteries, the annual discount rate and the increases or decreases in the retail prices of grid electricity and the feed-in tariff. The analyses are conducted using actual PV energy and smart meter data from a real case study house in Geelong, Australia. Results indicate that when battery capacity is increased, PV self-consumption and self-sufficiency grow under both tariff policies, but this trend is limited by constrained PV generation due to seasonal conditions. Additionally, increasing solar PV system size for fixed battery capacity increases PV self-sufficiency, but decreases PV self-consumption. Results of economic analysis demonstrate that the payback period for a standalone solar PV system increases as its capacity grows. Moreover, the payback period for PV batteries can be slightly shorter or even longer than using solar PV systems alone under both tariff policies, which is economically unattractive. Considering the benefits that batteries can bring to residents and electricity networks, local governments need to be more proactive in providing financial subsidies for residents to install batteries.

Optimal planning and configuration of adiabatic-compressed air energy storage for urban buildings application: Techno-economic and environmental assessment

As urbanization and demand for energy increase, the importance of localized renewable energy resources and energy storage system solutions becomes more prominent. Adiabatic-compressed air energy storage (A-CAES) has been identified as a promising option, but its effectiveness in decentralized applications is not widely concerned. This study aims to plan and design a decentralized A-CAES system to enhance its significance within urban building infrastructure. The focus is on the optimal design of a decentralized A-CAES system for urban building utilization through a comprehensive analysis of its thermodynamic, techno-economic, and environmental aspects. A sizing-designing approach inducing simulation and optimization is proposed to customize the A-CAES system according to the specific application requirement. To do so, this study investigates several energy management operation strategies (EMOS) toward different potential applications of decentralized A-CAES to maximize the system's value and adaptability during the project's lifetime. Multiple scenarios concerning managing solar PV-surplus power are proposed to investigate the overall performance of the proposed model. Additionally, a sensitivity analysis (post-optimization) is conducted to ensure the model's applicability across different case studies. The results demonstrate that an energy cost saving in the range of 0.015–0.021 $/kWh is achieved for the optimal hybrid system in which the A-CAES system is planned for solar photovoltaic (PV) integration and seasonal load shifting, leading to shaving the grid peak demand. Adopting such an A-CAES-PV hybrid system allows for achieving a 52 % electrical load management ratio and 65 % carbon emission reduction compared to the primary power system (grid). Furthermore, under the worst-case scenario (zero selling back), such an optimal hybrid energy system (HES) achieves a PV self-consumption rate of around 92 % and a payback time of 15.5 years. This analysis provides useful insights for policymakers, building owners, and energy planners interested in implementing sustainable and energy-efficient solutions, especially in the feasibility study phase.

Economic Viability Assessment of Neighbourhood versus Residential Batteries: Insights from an Australian Case Study

Amidst the evolving paradigms of the contemporary energy landscape, marked by the imperative of sustainability and efficiency, the integration of energy storage has emerged as a transformative strategy that seeks to recalibrate the dynamics of electricity distribution and consumption. However, there remains a pressing need to determine the most economically viable approach for deploying energy storage solutions in residential low-voltage (LV) feeders, especially in rural areas. In this context, this paper presents the results of an economic evaluation of energy storage solutions for a residential LV feeder in a rural town in Australia. Specifically, the study compares the financial viability of a front-of-the-meter (FTM) battery installed on the feeder with that of a fleet of behind-the-meter (BTM) batteries. The FTM battery, with a size of 100 kW/200 kWh, is assumed to be operated by the retailer but owned by the community, with any profits assigned to the community. In this scenario, we studied a battery operating under standard network tariffs and three different trial tariffs that distribution network service providers currently offer in Australia. On the other hand, the fleet of BTM batteries (3 kW, 3.3 kWh) are individually owned by households with solar installations, and their cumulative capacity matches that of the FTM battery. The comparison is based on key economic parameters, including network charges, retail margins, frequency control ancillary service (FCAS) revenues, wholesale energy costs, technology costs associated with community batteries, and net profit or loss for the community, as well as considerations of utility grid arbitrage and solar photovoltaic (PV) self-consumption. The study also assumes different grant levels to assess the impact of subsidies on the economic feasibility for both battery configurations. The findings indicate that, while both require some form of subsidy for profitability, the BTM batteries outperform the FTM battery in terms of economic viability and so would require lower grant support. The FTM battery case finds a need for grants ranging from 75% to 95% to break even, while the BTM fleet requires approximately 50% in grants to achieve a similar outcome. In conclusion, this study highlights the importance of grant support in making energy storage solutions economically feasible. In particular, it highlights how the less mature segment of FTM batteries will need higher support initially if it is to compete with BTM. The outcomes of this study inform decision-making processes for implementing energy storage solutions in similar communities, fostering sustainable and cost-effective energy systems.

Dual-layer optimization design method for collaborative benefits of renewable energy systems in building clusters: Case study of campus buildings

Renewable energy sharing effectively enhances the collaborative benefits of building clusters. Distributed photovoltaic (PV) generation is the most widely applied renewable energy method for building clusters due to its high space utilization and broad applicability. However, the majority of building cluster planning and energy storage system designs tend to neglect the impact of energy sharing potential, thereby leading to limited improvements in cluster-level benefits and oversized storage capacity. Therefore, we propose a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Firstly, the building cluster is randomly divided into Clustered Building Groups (CBGs). Secondly, linear programming (LP) is applied in the under-layer to optimize the shared energy storage capacity for each CBG. Finally, the upper-layer utilizes NSGA-II and TOPSIS to identify the optimal grouping scheme. Case studies on a building cluster showed that compared with two existing design methods, the proposed design could maximize the energy sharing potential, significantly improve the building cluster’s performance, and reduce the energy storage capacity. Specifically, it achieved the highest TOPSIS score of 0.673, reduced the electricity purchase cost by 4.6 %, increased PV self-consumption by 15.4 %, reduced the storage capacity by 17.4 %, and shortened the payback period from 9.31 years to 4.33 years. These results suggest that employing similar procedures can be advantageous in achieving energy autonomy for building clusters.

A hybrid heuristic-reinforcement learning-based real-time control model for residential behind-the-meter PV-battery systems

The behind-the-meter (BTM) energy management problem has recently attracted a lot of attention due to the increase in the number of residential photovoltaic (PV)-battery energy storage systems (BESSs). In this work, the use of deep reinforcement learning (DRL) combined with a novel heuristic model for real-time control of home batteries is investigated. The control problem is formulated as a finite Markov Decision Process with discrete time steps, where a proximal policy optimization (PPO) algorithm is employed to train the DRL agent with discrete action space. The agent is trained using real-world measured data to learn the policy for sequential charge/discharge tasks, aiming to minimize daily electricity costs. The battery power is calculated using an innovative heuristic model considering the agent's decision and the battery's available capacity, ensuring demand-supply balance through PV self-consumption and load demand shifting. The performance of the model is evaluated by comparing it to four RL agents and two benchmark models based on rule-based and scenario-based stochastic optimization strategies. The results confirm that the presented model outperforms its counterparts, offering €80.38 savings on electricity bills over 46 days of the test data set. This figure exceeds the savings of the rule-based and stochastic models by €15.64 and €19.38, respectively.

Profitability of Alternative Battery Operation Strategies in Photovoltaic Self-Consumption Systems under Current Regulatory Framework and Electricity Prices in Spain

The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector. The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide.

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.

Assessing urban rooftop PV economics for regional deployment by integrating local socioeconomic, technological, and policy conditions

Assessing the urban rooftop photovoltaic (PV) economics is important for scaling up rooftop PVs for rapid decarbonization. In this study, socioeconomic, technological, and policy factors were integrated into a rooftop PV economic assessment. This comprehensive method was applied to 21 cities in the Guangdong province of China, in combination with local real parameters. The results show that, with higher urbanization levels, electricity tariffs, and PV self-consumption rates, cities in the Pearl River Delta area generally had better economic performance in terms of both economic scale and profitability. They accounted for 75% of the total rooftop PV potential in Guangdong Province and had an internal rate of return (IRR) as high as 14.6–19.2% for Commercial and Industrial buildings (C&I), and 9.9–15.9% for residential buildings (R). Socioeconomic, technological, and policy factors jointly affect the economic performance and create a promising future for Guangdong rooftop PVs. It is projected that the IRR of current worst performing cities will grow by 4.9–5.8, and 4.4–5.9 percentage points, and the payback period will be shortened by 5.8–6.7 and 3.4–4.1 years by 2030 for R and C&I buildings, respectively. Among the three factors, technological advancement had the most significant long-term effect, followed by socioeconomic development. Policy factors play an important role in alleviating short-term cash flow pressure. Integrating the three factors in the assessment provides us with a more comprehensive picture of how urban rooftop PV economics will evolve and a better idea of how to prioritize rooftop PV development. Suggestions for prioritizing the deployment of rooftop PV systems in Guangdong by 2030 were provided.