Size Of Battery Storage Research Articles

Multi-objective sizing and dispatch for building thermal and battery storage towards economic and environmental synergy

The role of building thermal and battery storage is pivotal in advancing smart cities and achieving sustainability goals through effective energy management. Despite their significance, there are several limitations in the sizing approach and value stream analysis with various objectives for their widespread adoption in buildings. This work proposes a flexible and scalable multi-objective optimization framework for optimal sizing and dispatch of building thermal and battery storage, addressing multiple objectives simultaneously using mixed-integer linear programming. The weighted-sum method is adapted, combining multiple objectives into a single function. The two-stage procedure iterates over different weights, generates optimal solutions, and forms the Pareto front. Case studies are performed to assess the energy, economic, and environmental benefits of building energy storage systems for a large office building in three climate locations. The results demonstrate that the proposed framework efficiently determines optimal sizing and dispatch strategies, addressing the balance between economic viability and emission reduction. The dynamic relationship between time-of-use energy charges and emission factors leads to diverse strategies based on whether economic or environmental concerns are prioritized. This research enhances our understanding of the benefits of thermal and battery storage systems in buildings, providing valuable guidance to stakeholders.

Optimization of Electrical and Thermal Storage in a High School Building in Central Greece

Nearly zero-emission buildings (nZEBs) are increasingly being constructed in Europe. There are also incentives to refurbish older buildings and transform them into nZEBs. However, permission is not always granted for their connection to the grid to infuse surplus photovoltaic electricity due to the grid being overloaded with a large number of renewables. In this study, the case of a refurbished school building in Central Greece is examined. After refurbishing it, a significant amount of photovoltaic electricity surplus is observed during the summer and neutral months, which cannot be exported to the grid. The absence of an adequate battery storage capacity resulted in the rejection of an application for exporting the school’s surplus to the network and the photovoltaic installation staying idle. An alternative approach is proposed in this work, involving a shift in the export of the photovoltaic electricity surplus to the evening hours, in order for the school to be granted permission to export it to the network. To this end, an optimal battery storage size is sought by employing a building energy system simulation. The mode of operation of the battery designed for this application is set to discharge daily, in order to export the electricity surplus in the afternoon hours to the evening hours, when it is favorable for the network. Additionally, the optimal size of the thermal energy storage of the heating system is studied to further improve its energy efficiency. Our battery and storage tank size optimization study shows that a significant battery capacity is required, with 12 kWh/kWp photovoltaic panels being recommended for installation. The ever-decreasing cost of battery installations results in the net present value (NPV) of the additional investment for the battery installation becoming positive. The solution proposed forms an alternative path to further increase the penetration of renewables in saturated networks in Greece by optimizing battery storage capacity.

Batteries or silos: Optimizing storage capacity in direct air capture plants to maximize renewable energy use

Direct air capture (DAC) of carbon dioxide is among the technologies that is forecast to play a major role in achieving the global ambition to constrain atmospheric temperature rise to below two degrees Celsius by 2100. However, DAC is an energy intensive chemical process, whose designs are currently incompatible with intermittent renewable energy (RE) sources. This research develops a model to enable the flexible operation of DAC, to maximize RE usage. A new model of the chemical process flow of a liquid solvent DAC that includes silos to store CaCO3 and CaO is developed. A linear programming optimization model that minimizes energy costs while achieving the CO2 capture targets of the DAC plant is developed. Scenario analysis establishes the storage silo size and battery storage size needed to reduce renewable energy curtailment to zero for a given RE profile. Simulations with a representative 336-hour RE profile reveal that two silos of sizes 660 tons and 370 tons would be needed to support flexible DAC plant operations and reduce RE curtailment to zero. For the same profile, a 355 MWh/65 MW battery would be required to achieve zero renewable curtailment. The results demonstrate that flexible operation of DAC is achievable, and DAC plants can adapt to variable RE without the need for battery energy storage. Furthermore, considering the scale of storage needed to minimize RE curtailment in a commercial-scale DAC plant, the results suggest that using physical storage silos could be more cost-effective than using battery energy storage.

Facilitation of energy storage services in Northern Ireland

Along with many network operators across the globe, NIE Networks is facing challenges with the increased penetration of intermittent renewable generation, as well as changes in customer behaviour in response to the uptake of Low Carbon Technologies (LCT). NIE Networks recognises that electricity storage can be a means of alleviating network constraints and facilitating the connection of distributed generation and other LCTs. The FESS (Facilitation of Energy Storage Services) project sought to advance the mutual benefits that storage can provide to both the network and storage providers. Based on identified network constraints, six use cases for storage to provide services to the network were developed. These use cases were then further developed by determining the storage requirements for each use case example using a battery storage sizing optimisation model, based on a years' worth of half hourly data and the definition of a threshold. Through this assessment the mutual benefits of electricity storage services to both the network and storage providers has been evaluated. The project took a stakeholder centric approached, and through the identification of barriers has developed mitigating solutions including in the areas of network policy, information transparency and storage charging and settlement.

Sizing of Rooftop PV Array and Community-Run Battery Storage for an Energy Cooperative in Prosumer Cluster

A standalone system can address the problem of uncovered electricity from the grid. The cost of energy storage for installing renewable energy systems is one of the issues of such a system. This paper introduces and investigates the optimal capacity of a novel energy cooperative system with prosumer clusters and a community battery bank as typical energy storage. The system’s function is formulated to minimize the investor’s annual expenditure. The proposed energy cooperative system uses actual annual solar insolation data and the electric load demand of houses in the optimization process. The model, as mentioned above, is applied to two system configurations – energy cooperative without and with Prosumer to Prosumer (P2P) energy sharing. The reliability factor Loss of Power Supply Probability (LPSP) from the Cooperative Energy Sharing algorithm is taken as a constraint in the formulation. The comparison of the two configurations brings out the importance of P2P energy sharing in a standalone Energy Cooperative system. Particle Swarm Optimization (PSO) algorithm is used to achieve this optimization. The PSO results show that the proposed Energy Cooperative configurations are promising to facilitate the system’s reliability.

Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty

A large-scale renewable-based sustainable power system requires multifaced techno-economic optimization and energy penetration. Due to the volatile and non-periodic nature of renewable energy, the uncertainty of renewables combined with load uncertainties significantly impacts the operational efficiency of renewable integration. The complexities in balancing demand, generation, and maintaining system reliability have introduced new challenges in the current distribution system. Most of the associated challenges can be effectively reduced by using a battery energy storage system (BESS) and the right techniques for handling uncertainties. In this paper, a distributionally robust optimization (DRO) technique with a linear decision rule is formulated for the unit commitment (UC) framework for optimal scheduling of a distribution network that consists of a wind farm, solar PV, a distributed generator (DG), and BESS. To cut the energy cost per unit, BESS plays an important role by storing energy at an off-peak time for on-peak-time use with relatively lower prices. For the all-time minimum overall systems cost, the distribution system requires an optimal size of the BESS to be connected to provide optimal scheduling of DGs. Three case studies are formulated using an IEEE 14 bus system (converted from MW to kW to match the BESS size available in the market) and solved with the proposed distributionally robust optimization technique to achieve the maximum operating point with an optimal capacity of BESS, i.e., wind, solar and hybrid. Each case study has its own optimal 30-min interval schedule for DGs along with the optimal capacity of BESS. The cost comparison with and without BESS and its impact on the start-up and shut down of DGs is reported with all the dynamic economic dispatch results, including the battery’s state-of-charge profile. The proposed technique can handle the uncertainties in renewables and allows economical energy dispatch and optimal BESS sizing with comparatively lower computational processing and complexities.

Scenario-based techno-reliability optimization of an off-grid hybrid renewable energy system: A multi-city study framework

With the intrinsic volatile nature of renewable resources, it is necessary to evaluate the reliability aspect of renewable energy systems to withstand unexpected events in intensely loaded power systems. However, most of the studies mainly considered the loss of supply probability as a reliability criterion but neglected the effect of required operating reserve that is critical for the system’s reliability under unexpected events. In this paper, a techno-reliability optimization framework is proposed to incorporate the impact of reliability constraints during the operation and planning phase of hybrid renewable energy systems. This multi-objective framework considers a scenario-based approach under different levels of peak load and load reserves that are further evaluated by a unified weightage index (techno-economic-reliability index) to find out the optimal reliability level while considering the trade-off between techno-economic and reliability indicators. As a case study, the proposed framework is implemented in multiple locations in Pakistan to investigate the correlation between system evaluation indicators and the variation of available resources for different system configurations. The results indicate that the load reserve-based scenario (S2) is the most feasible operating reserve strategy to obtain the optimal system configuration in terms of techno-economic-reliability nexus. The sensitivity analysis of various system and economic evaluation indicators shows that the levelized cost of energy is more sensitive to the available site resources and discount rate. Moreover, the system reliability level has the least effect on the size of battery storage. This paper provides a concept of the micro-grid value proposition for the energy policymakers to select the appropriate multi-objective constraints while prioritizing the trade-off between techno-economic and reliability indicators.

Optimal sizing of stationary battery storage taking into account indirect flexibility in tertiary buildings: use case of an electric vehicle community

This article proposes a method for taking into account the indirect flexibility (charging of Electric Vehicles) available in buildings for the sizing of stationary battery storage systems (direct flexibility). A Linear Programming approach was applied to data sourced from the Predis-MHI platform (a living lab) such that the day-to-day charging of EVs, as well as the scheduling of the charging and discharging of the proposed battery, were optimized whilst simultaneously dimensioning the battery capacity. Our results indicate that based on the percentage increase in self-consumption with reference to the base case, it is possible to reduce the battery capacity required by up to 100% as compared to a methodology that doesn't account for the indirect flexibility. Whilst relevant, the sizing approach proposed in this article assumes optimal human behavior, which is usually difficult to achieve. Our proposed approach can be adapted and used for the dimensioning of direct flexibilities for both residential and commercial/public buildings.

Comparative analysis of solar - battery storage sizing in net metering and zero export systems

Solar energy is an intermittent as well as a variable resource. The integration of battery energy storage systems (BESS) with solar photovoltaic (PV) systems can help to mitigate some of the shortcomings of solar energy. In India, many states have a provision for net metering for solar projects. For states that do not allow net metering, a zero-export (grid export curtailed) system emerges as the preferred configuration. This paper presents a techno-commercial comparison of net metering and zero-export systems. An AC coupled grid-connected PV-BESS hybrid system is set up as our reference case so that the PV and battery components can be decoupled. Commercially available components used in actual installations have been selected to enable us to get realistic results. Financial metrics like levelized cost of energy (LCOE) and benefit cost ratio (BCR) are utilized to determine the viability of different system configurations. The optimum size of solar PV, as well as the capacity of BESS which can be integrated with solar PV, is derived, under net metering as well as zero-export regimes. For a 300 kW system, 51% of energy is curtailed in a zero-export system, as compared to a net metering system which has no curtailment.

Quantifying the effects of forecast uncertainty on the role of different battery technologies in grid-connected solar photovoltaic/wind/micro-hydro micro-grids: An optimal planning study

Designing a reliable and robust micro-grid (MG) aided by energy storage devices requires quantifying the parametric uncertainty associated with input data time-series, among other types of uncertainties – and in particular, the uncertainty in forecasted meteorological, load demand, and wholesale electricity price time-series. Given the computational complexities involved, the recent battery-storage-supported MG capacity optimisation literature fails to simultaneously characterise multiple sources of data uncertainty. This paper, therefore, seeks to address this knowledge gap by hedging against a wide array of parametric uncertainties at the same time, whilst exploring the potentially salient implications of high-dimensional uncertainty characterisation for the costing and configuration of battery-supported, grid-tied MGs. To this end, this paper introduces a novel computationally efficient, stochastic MG design optimisation model that enables the simultaneous handling of multiple uncertain inputs. Notably, the model yields in-depth, accurate, and robust infrastructure decision-making support, particularly with regard to the optimal size of battery storage, by specifically analysing the worst-case, most likely case, and best-case uncertainty characterisation scenarios. To demonstrate the effectiveness of the model within a community renewable energy project scheme, a case study of a grid-connected, battery-storage-supported MG in the town of Ohakune, New Zealand is presented. Importantly, the numerical simulation results indicate that the life-cycle cost of the MG would have been underestimated by as much as ~8% (equating to NZ$0.3 m) in the most likely case – with an associated ~15% (equating to 143 kWh) battery size underestimation – if the variability inherent in forecast inputs was not factored into the analysis. Also, the proportion of the energy storage capacity to the total renewable power generation capacity is ~71%, ~76%, and ~68% in the best-case, most likely case, and worst-case scenarios, respectively, suggesting that the risk-seeking and risk-averse MG planning strategies have a slightly contradictory effect on the storage-to-generation ratio. Comprehensive sensitivity analyses of different battery technologies with different specific power, specific energy, and depth-of-discharge specifications have also demonstrated the robustness and validity of the model, whilst additionally identifying the sodium‑sulfur (NaS) battery technology as the most profitable choice in grid-connected MG development applications – which can be primarily attributed to the fact that it provides the best trade-off between the specific power and specific energy.

Optimal battery sizing for a grid-tied solar photovoltaic system supplying a residential load: A case study under South African solar irradiance

Owing to the global increasing need for clean renewable energy, solar photovoltaic (PV) generation technology has gained more attention. The utilization of a grid-tied solar PV rooftop system may minimize the electricity bills of residential consumers. Battery storage proved to be the most expensive component of a solar PV system. Hence, optimal battery sizing for a grid-tied PV solar system is of fundamental importance to maximize investment returns. This study aims to determine the optimal battery size for the proposed non-interactive grid-tied solar PV-battery system when exposed to South African solar irradiance. The proposed system is investigated for supplying the residential load under the time-of-use (TOU) pricing strategy. Hence, the optimal power flow control model has been developed and utilized to determine the optimal battery size. Optimal power flow management has been achieved through the use MATLAB optimization solver called linprog. Different battery sizes have been analyzed for the selected 4.2-kW solar PV array that supplies a residential load having a peak demand of 4.2-kW. The optimization results indicated that the optimal battery size is 18.3% of the residential load demand, in the context of South African solar irradiance and the TOU tariff scheme. When the selected PV array size matches the peak load demand, the selection of a battery size greater than 18.3% proved to minimize the economic returns as a result of the inflexible electricity cost savings. For PV array size greater than the peak load demand, the optimal battery storage size increases by 11.5% of the daily load energy consumption per kW upsizing.

Homogenising the Design Criteria of a Community Battery Energy Storage for Better Grid Integration

Historically, minimum system demand has usually occurred overnight. However, in recent years, the increased penetration of rooftop photovoltaic systems (RPVs) has caused an even lower demand at midday, forcing some of the conventional generators to shut down only hours before the evening peak demand period. This further complicates the job of power system operators, who need to run the conventional generator at the minimum stable level at the midday low-demand period so that they can reliably supply power during the peak periods. Employing a community battery storage system can alleviate some of the technical issues caused by the high penetration of RPVs. This paper proposed a design criterion for community battery energy storage systems and employed the battery for the improvement of the duck curve profile and providing the desired level of peak-shaving. Furthermore, remote communities with high penetration of RPVs with a community battery energy storage can achieve the desired level of self-sufficiency. To this end, this study recommends and confirms an applicable design criterion for community battery energy storage. The study shows that the suitable size of community battery storage should be based on the community’s daily excess generation and consumption requirements. The results of various scenarios performed on the proposed design criterion show the extent to which the desired objectives of peak-shaving, duck curve mitigation, and self-sufficiency can be achieved.

MULTI-PERIOD OPTIMAL POWER FLOW IN LOW VOLTAGE GRIDS FOR A HIGH DEGREE OF SELF-SUFFICIENCY

This paper investigates the application of AC multi-period optimal power flow in local energy communities for a high degree of self-sufficiency using a combination of a hydrogen storage system and a battery storage. A residential subnetwork with installed photovoltaic systems and a central storage within the CIGRÉ low voltage network is taken as reference. An electrolyser, a fuel cell and a hydrogen pressure storage are added to the model, which determines the global solution of the multi-period optimal power flow problem. Different households and photovoltaic power supply scenarios are analysed and seasonal influences considered for an optimization horizon of an entire year. Results show that photovoltaic and battery storage size decrease significantly by using a seasonal storage. Paper's second part deals with optimal operation for both cases. It becomes clear that the H2 storage has a positive influence on both: operation of the technologies and operation of the network. Economic benefits of installing a combined energy system is currently not given, but may be the case if technology costs decrease.

A Demand Side Management Approach For Optimal Sizing of Standalone Renewable-Battery Systems

This paper develops a novel demand side management (DSM) approach to incorporate in optimal sizing of solar photovoltaic (PV), wind turbine (WT), and battery storage (BS) for a standalone household. The DSM strategy is based on the state-of-charge level of battery and day-ahead forecasts of solar insolation and wind speed. The core of the DSM is a fuzzy logic method which decides for efficient load shifting and/or load curtailment. The day-ahead forecasting errors, obtained by an artificial neural network technique, are considered not only in the DSM strategy but also in maintaining an operating reserve. The battery capacity degradation is calculated using the Rainflow counting algorithm to obtain a realistic battery model and estimate its lifetime. A typical household in South Australia (SA) is considered as a case study. Three different configurations (PV-BS, WT-BS, and PV-WT-BS) of the electricity supply system are optimized using the proposed method. It is found that the PV-WT-BS system is the best configuration that provides the lowest cost of electricity for both with and without applying the proposed DSM strategy. Comparison of the results of the best system configuration with an actual system in SA and two recently published articles indicates that the proposed method is very effective in lowering the electricity cost with zero-emission.

Life Cycle Assessment for Supporting Dimensioning Battery Storage Systems in Micro-Grids for Residential Applications

The current EU energy and climate policy targets a significant reduction of carbon dioxide emissions in the forthcoming years. Carbon pricing, embedded in the EU emissions trading system, aims at achieving emission reductions in a more evenly spread way and at the lowest overall cost for society, compared with other environmental policy tools, such as coal or electricity taxes, or incentives such as subsidies on renewables. Still, the implementation of the decarbonization policy depends on several technical parameters such as the type, size and connectivity of the energy system as well as economic restrictions that occur. Within this paper, an optimization tool will be presented, focusing on cleaner energy production and on the control and reduction of environmental impact regarding energy storage solutions. Various types of batteries are examined and evaluated towards this direction. Emphasis is given to setting new criteria for the decision-making process, considering the size of battery storage and the selection of the battery type based on the environmental impact assessment parameter. The objective function of the system is formulated so as to evaluate, monitor and finally minimize environmental emissions, focusing mainly on carbon emissions. Optimization is carried out based on mixed integer nonlinear programming (MINLP). Two of the main battery types compared are lead–acid and lithium-ion; both of them result in results worth mentioning regarding the replacement impact (seven times during system lifetime for lead–acid) and the total environmental impact comparison (lithium-ion may reach a 60% reduction compared to lead–acid). Case studies are presented based on representative scenarios solved, which underline the importance of choosing the appropriate scope for each case and demonstrate the potential of the tool developed, as well as the possibilities for its further improvement.

Strategies of Design Concepts and Energy Systems for Nearly Zero-Energy Container Buildings (NZECBs) in Different Climates

Container-based lightweight buildings offer a high ecologic and economic potential when they are designed as nearly zero-energy container buildings (NZECBs). Thus, they are relevant to energy transition in achieving an almost climate-neutral building stock. This paper describes and applies design strategies for suitable building concepts and energy systems to be used in NZECBs for different climates. Therefore, different applications in representative climatic zones were selected. Initially, the global climate zones were characterized and analyzed with regard to their potential for self-sufficiency and renewable energies in buildings. The design strategies were further developed and demonstrated for three cases: a single-family house in Sweden, a multi-family house in Germany, and a small school building in rural Ethiopia. For each case, design guidelines were derived and building concepts were developed. On the basis of these input data, various energy concepts were developed in which solar and wind energy, as well as biomass, were integrated as renewable energy sources. All the concepts were simulated and analyzed with the Polysun® software. The various approaches were compared and evaluated, particularly with regard to energy self-sufficiency. Self-sufficiency rates up to 80% were achieved. Finally, the influence of different climate zones on the energy efficiency of the single-family house was studied as well as the influence of the size of battery storage and insulation.

Renewable Energy Communities: Optimal sizing and distribution grid impact of photo-voltaics and battery storage

In this paper, an extensive study of Renewable Energy Communities and their potential impact on the electric distribution grid has been carried out. For that purpose, a Linear Programming optimization model sizing the energy community’s Photo-Voltaic and Battery Energy Storage System was developed. The linear programming model was soft coupled with power flow analysis to investigate the impact of different energy community configurations on distribution grids. Different distribution grids (city, suburban, village), different energy community configuration, different operating strategies and different battery placements were investigated. The results showed that when the battery is located at the beginning of the feeder, then the energy community does not impact the observed minimum and maximum voltage. Moreover, it was found that depending on the energy community’s operating strategy the low-voltage grid loading can be reduced by up to 58 %. The energy community’s sizing showed that optimal capacities of photo-voltaics and communal batteries were up to three times larger for the case of city grid, following the operating strategy of maximizing the energy community’s own economic benefit than in other operating strategies and grid types.

Optimal Sizing of Rooftop PV and Battery Storage for Grid-Connected Houses Considering Flat and Time-of-Use Electricity Rates

This paper investigates a comparative study for practical optimal sizing of rooftop solar photovoltaic (PV) and battery energy storage systems (BESSs) for grid-connected houses (GCHs) by considering flat and time-of-use (TOU) electricity rate options. Two system configurations, PV only and PV-BESS, were optimally sized by minimizing the net present cost of electricity for four options of electricity rates. A practical model was developed by considering grid constraints, daily supply of charge of electricity, salvation value and degradation of PV and BESS, actual annual data of load and solar, and current market price of components. A rule-based energy management system was examined for GCHs to control the power flow among PV, BESS, load, and grid. Various sensitivity analyses are presented to examine the impacts of grid constraint and electricity rates on the cost of electricity and the sizes of the components. Although the capacity optimization model is generally developed for any case study, a grid-connected house in Australia is considered as the case system in this paper. It is found that the TOU-Flat option for the PV-BESS configuration achieved the lowest NPC compared to other configuration and options. The optimal capacities of rooftop PV and BESS were obtained as 9 kW and 6 kWh, respectively, for the PV-BESS configuration with TOU-Flat according to two performance metrices: net present cost and cost of electricity.

Techno-economic design of energy systems for airport electrification: A hydrogen-solar-storage integrated microgrid solution

Can aviation really become less polluting? The electrification of airport energy system as a micro-grid is a promising solution to achieve zero emission airport operation, however such electrification approach presents the engineering challenge of integrating new energy resources, such as hydrogen supply and solar energy as attractive options to decarbonize the present system. This paper explores the techno-economic benefits of integrating hydrogen supply, electric auxiliary power unit (APU) of aircraft, electric vehicles, photovoltaic energy (PV), and battery storage system into electrified airport energy system. The hydrogen fuel cell generation provides great flexibility to supply aircraft at remote stands, and reduces the carbon emissions caused by traditional fuel-powered APU. A mixed integer linear programming optimization method based on life cycle theory is developed for capacity sizing of hydrogen energy system, PV and battery storage, with optimization objective of minimizing the total economic costs as well as considering environmental benefits of the proposed airport microgrid system. Case studies are conducted by five different energy integration scenarios with techno-economic and environmental assessments to quantify the benefits of integrating hydrogen and renewable energy into airport. Compared with the benchmark scenarios, the integration of hydrogen energy system reduced the total annual costs and carbon emissions of airport energy system by 41.6% and 67.29%, respectively. Finally, sensitivity analysis of key system parameters such as solar irradiance, grid emission factor, elctricity price, carbon tax, unit investment cost of hydrogen energy system have been investigated to inform the design of hydrogen-solar-storage integrated energy system for future airport electrification.

Techno-Economic Viability of Large Scale Solar Integration with Battery Storage for Grid Substations: A Case Study for Sri Lanka

Over the past few years, utility scale Solar PV Power plants (SPVPs) are being added to the national grid at Medium Voltage (MV) distribution networks. However, the performance of the distribution networks can adversely be affected, if they are connected without the knowledge of optimum sizes and locations. Challenge in utilization of electricity generated from the weather dependent SPVPs is in its intermittency and non-dispatchability, rendering it hard to match supply and demand which themselves are variable. The difficulties associated with proliferation of SPVPs could be alleviated by the proper use of Battery Energy Storage Systems (BESSs). The impact of installing BESS on the quality of distribution networks during the sizing of battery storage has been considered in this research. In this paper, the optimal placement of SPVPs and B-SPVPs in terms of size and location is evaluated for minimisation of energy loss bounded by voltage constraints preserving the power balance. The required optimization is carried out using Mixed Integer Programming with Genetic Algorithm (MIGA) and Particle Swarm Optimization (PSO) techniques. The proposed approach was utilised to study the techno economic viability of integration of solar Photovoltaic (PV) and battery energy storage systems to a 33 kV practical network in Sri Lanka – Tissa 1 feeder in Hambantota Grid Substation (GSS). A financial evaluation was carried out to inspect the viability of SPVP and B-SPVP in Tissa 1 feeder using the optimized results.