Methods of energy management and of sizing energy storage systems for installation on a site comprising a power generation source

Abstract

A method of sizing energy storage systems for installation on a site comprising a power generation source is provided, the method comprising obtaining an indication of predicted surplus power generati

Description

Field of the invention The present disclosure relates to methods of energy management and of sizing energy storage systems for installation on a site comprising a power generation source, in particular relating to a battery energy storage system. Background In recent years, electrification has become a major strategy to decarbonise and achieve NetZero targets. However, there are increasing concerns about how existing electricity grid infrastructure will cope with electric vehicles, as well as increased demand from domestic, commercial, and industrial usage facilitated through electrification. Geo-political tensions have also raised concerns relating to energy availability dependent on fossil fuels, or other nation’s resources. As such, there is a need to reduce strain on existing grid infrastructure, whilst also providing clean, renewable energy locally. Summary of the invention Aspects of the invention are as set out in the independent claims and optional features are set out in the dependent claims. Aspects of the invention may be provided in conjunction with each other and features of one aspect may be applied to other aspects. An aspect of the invention relates to a method of sizing energy storage systems for installation on a site comprising a power generation source. In particular, the invention may relate to a method of sizing energy storage systems for installation on a site comprising a renewable power generation source. The method comprises: obtaining an indication of predicted surplus power generation by the power generation source; determining a required capacity of an energy storage system based on the predicted surplus power generation to minimise energy export to grid; obtaining an indication of predicted number of electric vehicles on the site available for vehicle-to-everything, V2X, utilisation; determining a usable capacity of the electric vehicles on the site available for V2X utilisation, wherein the usable capacity of the electric vehicles is based on the number of electric vehicles predicted to be on the site, the total capacity of each electric vehicle, and an electric vehicle state of charge window restriction; and sizing an energy storage system capacity for installation based on (i) the required capacity of an energy storage system, and (ii) the usable capacity of the electric vehicles on the site available for V2X utilisation. Sizing an energy storage system is important to determine the optimum size of energy storage required for a particular site, wherein the site is, at least in part, powered by an onsite power generation source, such as a renewable power source. If the energy storage system is too small for the site, this can result in excess power generated by the power generation source being offloaded to the grid without being utilised by the site during periods of high energy generation relative to demand, as well as continuing to rely on power draw from the grid by the site during periods of high demand relative to onsite power generation. An energy storage system that is too large for the site may be unnecessarily bulky and space-consuming on site, as well as being excessively costly. Sizing an energy storage system for installation on a site comprising a power generation source is also important for grid-side energy management. If energy storage systems are too small for a site, grid infrastructure may struggle to accommodate excess power being exported from the site to the grid. This often results in excess energy being dumped from the grid, for example in the event of high winds, surges in wind energy being exported to the grid may result in energy being offloaded and dumped. As such, it may be desirable to minimise power feed in in to the grid. A correctly sized energy storage system can store excess electricity generated on site and release it when needed, thus ensuring grid stability and reliability. A sized energy storage system may also be advantageous to make it less “noisy” on the grid by facilitating reactive power compensation. A correctly sized energy storage system may also reduce peak demand on the grid by enabling a site to store energy and raw power from the energy storage system during peak site demand, provide backup power to a site during outages, and / or enable load levelling and demand response. V2X technology may allow EVs to discharge their batteries into a site, effectively acting as a BESS for demand shifting. This enables the site to use the stored energy from EVs to meet its peak power demand, reducing its reliance on grid electricity. By reducing the site’s peak power demand, V2X can also contribute to improved grid stability and reduce the need for additional power plants. V2X technology may also allow EVs to feed electricity back into the grid. This bi-directional flow of energy can facilitate better grid management and enhance grid stability and reliability. The integration of V2X with PV systems, or other renewable power sources, may create a more sustainable energy management system. Surplus energy generated by the PV system, or other onsite renewable power sources, can be used to charge the EVs back to their original SOC. If there is any shortfall in the energy required to charge the EVs, the grid may be used to compensate. Sizing the energy storage system based on (i) the required capacity of an energy storage system, and (ii) the usable capacity of the electric vehicles on the site available for V2X utilisation may be advantageous to reduce the required size of an energy storage system for installation on site by incorporating the usable capacity of the electric vehicles on the site available for V2X utilisation into sizing. For example, sizing the energy storage system capacity may comprise calculating the required capacity of an energy storage system across a time period minus the usable capacity of the electric vehicles on the site available for V2X utilisation across the same time period. V2X utilisation may also be advantageous to reduce the reliance of a site on grid electricity by enabling use of stored energy from EVs to meet its peak power demand. The method may further comprise installing the energy storage system on the site, according to the determined size of the energy storage system capacity. In some examples, the energy storage system comprises a battery energy storage system (BESS). The method may further comprise determining a usable capacity of an energy storage system, wherein the usable capacity is based on the total capacity of an energy storage system and a state of charge window restriction; wherein sizing the energy storage system capacity is further based on the usable capacity of an energy storage system. This may be advantageous to extend and / or optimise the life of the energy storage system by implementing state of charge window restrictions, for example wherein the state of charge window restrictions represent at least one of a minimum state of charge operational threshold and / or a maximum state of charge operational threshold. This may be particularly advantageous for energy storage system comprises a BESS in order to reduce battery ageing and degradation by restricting the usable charge window. For energy storage systems comprising a BESS, the method may further comprise modelling the degradation of the BESS and determining capacity fade of the BESS based on the modelled degradation. Sizing the energy storage system for installation may further be based on the determined capacity fade of the BESS. This may be advantageous to accurately size the energy storage system for use over a time period, accounting for capacity fade and reduced performance of the BESS, in order to minimise power draw and pressure on the grid in future as a result of the capacity fade. Obtaining the indication of predicted surplus power generation may comprise predicting the amount of surplus power generation generated by the power generation source. This may be particularly advantageous for embodiment wherein the power generation source comprises at least one renewable power generation source because renewable power generation often relies upon natural resources which cannot be controlled, such as sunlight and wind power. Example power generation sources may include, but are not limited to, fuel generators, biomass generators, solar photovoltaics (PV), wind turbines, hydropowered turbines, etc. The method may further comprise obtaining an indication of predicted power demand of the site; determining a required capacity of power generation for the site, based on the indication of predicted power demand of the site; and determining the size of a power generation source required based on the required capacity of power generation. Optionally, the method may further comprise constraining the size of the power generation source by available area for installation at the site, wherein obtaining an indication of predicted surplus power generation by the power generation source is based on the constrained size of the power generation source. This may be advantageous to determine appropriate sizing of power generation sources for a site based on demand and, optionally, the available space, and size the energy storage system in accordance with the power generation source(s). The method may further comprise installing the power generation source, optionally based on the constrained size. For example, the method may further comprise obtaining an indication of predicted power demand of the site; determining a required capacity of power generation for the site, based on the indication of predicted power demand of the site; calculating a number of photovoltaic panels required based on the required capacity of power generation; and constraining the number of photovoltaic panels by available area for installation at the site. Obtaining the indication of predicted surplus power generation by the power generation source may then be based on the constrained number of photovoltaic panels. The method may further comprise installing the constrained number of photovoltaic panels. Obtaining the indication of predicted surplus power generation by the power generation source may further comprise obtaining an indication of predicted power demand of the site over a first time period; predicting rate of power generation by the power generation source over the first time period; identifying at least one second time period wherein the predicted rate of power generation is greater than the predicted power demand, wherein the second time period is a subset of the first time period; and scheduling operation of at least one piece of equipment on the site during the at least one second time period. This may be advantageous to efficiently schedule operations and equipment during periods where energy generation is predicted to be greater than demand, thereby reducing strain on the grid by scheduling operations and equipment during periods where power may otherwise need to be drawn from the grid to sustain the operations, whilst also reducing the amount of energy exported to the grid during times of excess power generation, for example to reduce instances of power dumping. The method may also further comprise updating the indication of predicted power demand of the site over the first time period based on the scheduling; and calculating an indication of predicted surplus power generation by the power generation source, based on the updated indication of predicted power demand and the predicting rate of power generation. The method may then re-size the energy storage system capacity for installation based on the updated predicted surplus power generation. This may be advantageous to minimise the required capacity of the energy storage system. In another aspect of the invention, there is provided a method of sizing photovoltaic (PV) energy generation systems for installation on a site, the method comprising obtaining an indication of predicted power demand of the site, determining a required capacity of power generation for the site, based on the indication of predicted power demand of the site, calculating a number of photovoltaic panels required based on the required capacity of power generation, and constraining the number of photovoltaic panels by available area for installation at the site. This may be advantageous to accurately size photovoltaic energy generation systems for installation on a site. Sizing photovoltaic energy generation systems is important to determine the optimum size of photovoltaic energy generation system required for a particular site. If the PV energy generation system is too small for the site, this can result in insufficient power generation and high operational reliance on the grid to draw power during periods of high demand relative to onsite power generation. If the PV energy generation system is too large, excess power generated by the PV energy generation system will be offloaded to the grid without being utilised by the site during periods of high energy generation relative to demand. Offloading large amounts of renewable power generation to the grid can strain the grid and result in power dumping. A PV energy generation system which is too large for the site may also be unnecessarily bulky and space-consuming on site, as well as being excessively costly. The method may further comprise predicting (or obtaining a prediction) of power generation across a first time period by the constrained number of PV panels at the site. The method may then comprise identifying at least one second time period wherein the predicted rate of power generation is greater than the predicted power demand for the site, wherein the second time period is a subset of the first time period. The method may further comprise scheduling operation of at least one piece of equipment on the site during the at least one second time period. This may be advantageous to efficiently schedule operations and equipment during periods where energy generation is predicted to be greater than demand, thereby reducing strain on the grid by scheduling operations and equipment during periods where power may otherwise need to be drawn from the grid to sustain the operations, whilst also reducing the amount of energy exported to the grid during times of excess power generation, for example to reduce instances of power dumping. The method may also further comprise updating the indication of predicted power demand of the site over the first time period based on the scheduling; and re-calculating a required capacity of power generation for the site, based on the updated indication of predicted power demand. The method may then re-size the number of photovoltaic panels required for installation based on the updated required capacity of power generation. This may be advantageous to minimise the required number of PV panels. The re-sized number may still be subject to constraining based on available area for installation. The method may further comprise installing the constrained number of photovoltaic panels. In another aspect of the invention, there is provided a method of energy management for a site comprising a power generation source and a battery energy storage system. The method comprises obtaining a state of charge of the battery energy storage system, determining the rate of power generation by the power generation source, determining the rate of power demand by the site, and: (i) in the event that the rate of power generation is higher than the rate of power demand and the state of charge of the battery energy storage system is below an upper threshold, charging the battery energy storage system using excess power generated by the power generation source, wherein the excess power generated is the remaining power in excess of the power demand; and (ii) in the event that the rate of power generation is lower than the rate of power demand and the state of charge of the battery energy storage system is above a lower threshold, discharging the battery energy storage system at a discharge rate, wherein the discharge rate is based on the difference between the rate of power generation and the rate of power demand. This may be advantageous to minimise power draw from the grid. This may help to stabilise operation of the grid, as well as minimising noise at the grid. Charging the battery energy storage system may comprise charging the battery energy storage system at a maximum charge rate. The method may further comprise determining that the rate of excess power generation is greater than the maximum charge rate, and exporting surplus energy. Surplus energy may be defined as the remaining power in excess of the power demand of the site and the maximum charge rate of the battery energy storage system. In the event that the difference between the rate of power generation and the rate of power demand is greater than a maximum discharge rate, the method may further comprise importing the remaining energy imbalance from the grid, wherein the remaining energy imbalance is the remaining power demand in excess of power generation and the maximum discharge rate. In the event that the rate of power generation is lower than the rate of power demand, and the state of charge of the battery energy storage system is equal to or below the lower threshold, the method may further comprise importing the remaining energy imbalance from the grid, wherein the remaining energy imbalance is the remaining power demand in excess of the power generation. In the event that the rate of power generation is higher than the rate of power demand, and the state of charge of the battery energy storage system is equal to or above the upper threshold, the method may further comprise exporting surplus energy, wherein surplus energy may be defined as the remaining power in excess of the power demand of the site. The battery energy storage system may comprise at least one stationary battery unit, and at least one electric vehicle on the site available for vehicle-to-everything, V2X, utilisation. Utilising a battery energy storage system comprising at least one electric vehicle on the site available for V2X utilisation may be advantageous to increase the capacity of the battery energy storage system available without increasing the capacity of the stationary battery unit which may be bult and space consuming. The upper threshold and / or lower threshold may be different for the at least one stationary battery unit and the at least one electric vehicle. The battery energy storage system may comprise a maximum charge rate, wherein the maximum charge rate for the at least one stationary battery unit, and the at least one electric vehicle are different. Alternatively, or in addition, the battery energy storage system may comprise a maximum discharge rate, wherein the maximum discharge rate for the at least one stationary battery unit, and the at least one electric vehicle are different. In another aspect of the invention, there is provided a method of scheduling energy use on a site comprising a power generation source and a battery energy storage system. The method comprises obtaining a prediction of (or predicting) power demand by the site over a first time period, obtaining a prediction of (or predicting) rate of power generation by the power generation source over the first time period; and obtaining a prediction of (or predicting) the state of charge of the battery energy storage system. The method further comprises identifying at least one second time period wherein the state of charge of the battery is above a mid-threshold, wherein the second time period is a subset of the first time period, and wherein the mid threshold is above the lower threshold, and equal to or less than the upper threshold, scheduling operation of at least one piece of equipment on the site during the at least one second time period, and scheduling discharge of the battery energy storage system during the at least one second time period, such that discharge of the battery is configured to at least partially power operation of the at least one piece of equipment. This may be advantageous to implement demand-side shifting to reduce peak demand of a site. Demand-side shifting via scheduling may also reduce reliance on the grid and optimise scheduling of equipment to be powered at least partially by the onsite BESS. This also reduces demand and strain on the grid-side infrastructure. Predicting the state of charge of the battery energy storage system, or obtaining a prediction of the state of charge of the battery energy storage system, may be determined based on the battery energy storage system being configured to: (i) charge the battery energy storage system using excess power generated by the power generation source if the state of charge of the battery energy storage system is below an upper threshold, wherein the excess power generated is the remaining power in excess of the power demand; and (ii) discharge the battery energy storage system if the state of charge of the battery energy storage system is above a lower threshold and in the event that the rate of power generation is lower than the rate of power demand, for example as described in the preceding aspect of the invention. The at least one second time period may be determined based on when the rate of power generation is predicted to be greater than the predicted power demand. Preferably, the method comprises rescheduling operation of at least one piece of equipment from a time period wherein the power demand is greater than the rate of renewable power generation relative to the second time period. The second time period may be a time period wherein the rate of power generation is predicted to be greater than the predicted power demand. Scheduling operation of equipment in this manner may be advantageous to reduce peaks in demand for the site and thereby reduce energy draw from and reliance on the grid. For example, scheduling operation of the equipment during the second period may facilitate operation of the equipment to be powered by at least one of (i) renewable power generation, for example by PV solar panels, and (ii) onsite energy storage, such as a BESS system. In another aspect of the invention, there is provided a method of scheduling energy storage on a site comprising a power generation source and a battery energy storage system, the method comprising obtaining a prediction of (or predicting) power demand by the site over a first time period; obtaining a prediction of (or predicting) rate of power generation by the power generation source over the first time period; and obtaining a prediction of (or predicting) the state of charge of the battery energy storage system. The method further comprises identifying at least one second time period wherein the rate of power generation is predicted to be greater than the predicted power demand, and the state of charge of the battery is below a threshold, wherein the second time period is a subset of the first time period; scheduling charging of the battery energy storage system during the at least one second time period; and implementing charging of the battery energy storage system according to the scheduling, such that the battery energy storage system is charged during the second time period. Obtaining the prediction of (or predicting) the state of charge of the battery energy storage system may be based on the functionality of the battery energy storage system, wherein the battery energy storage system is configured to (i) charge the battery energy storage system using power generated by the power generation source wherein the excess power generated is the remaining power in excess of the power demand; and (ii) discharge the battery energy storage system in the event that the rate of power generation is lower than the rate of power demand. In another aspect of the invention, there is provided a method for energy management of a site comprising a power generation source and a battery energy storage system to minimise power draw from the grid. The method comprises obtaining a prediction of (or predicting) power demand of the site across a first time period; obtaining a prediction of (or predicting) rate of power generation by the power generation source across the first time period; and obtaining a prediction of (or predicting) the rate of power draw from the grid across the first time period, based on the predicted power demand and predicting rate of power generation. The method further comprises simulating the amount of active power and reactive power required across the first time period, based on the predicted power demand and predicting rate of power generation; and simulating a state of charge of the battery energy storage system across the first time period based on the determined power demand and determined rate of power generation, wherein simulating the state of charge of the battery energy storage system comprises simulating drawing at least a portion of reactive power from the battery energy storage system to reduce the apparent power draw from the grid. The method may further comprise controlling the charge and / or discharge of the battery energy storage system based on the simulated state of charge of the battery energy storage system to reduce the to reduce the apparent power draw from the grid. In some examples, controlling the charge and / or discharge of the battery energy storage system comprises scheduling charge and / or discharge of the battery energy storage system across the first time period based on the simulated state of charge of the battery energy storage system to reduce the to reduce the apparent power draw from the grid. Drawings Embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings, in which: Fig. 1 shows a flow diagram illustrating an example method of sizing photovoltaic energy generation systems for installation on a site. Fig. 2A shows a satellite image of an example site. Fig. 2B shows the example satellite image of Fig. 2A, wherein the available area for installation of PV panels is estimated based on the satellite image. Fig. 3 shows a flow diagram illustrating an example method of sizing energy storage systems for installation on a site comprising a power generation source. Fig. 4 shows a graph illustrating the energy trends over a typical work week and weekends for a site, including (i) PV energy generation, (ii) power demand, and (iii) net energy generation based on the PV generation and the power demand. Fig. 5A shows a graph illustrating an example relationship between the gradient of export ratio against energy storage capacity to demonstrate how the capacity of an onsite battery storage system can reduce the amount of energy generation which is exported to the grid. Figs. 5B to 5E compare example simulated usage of a 1000kWh BESS and a 5000kWh BESS for the same loading. In particular, Figs. 5B and 5D each show the amount of energy exported to the grid when paired with a 1000kWh BESS and a 5000kWh BESS, respectively. Figs. 5C and 5D represent usage of the BESS capacity as a function of time for a 1000 kWh BESS and a 5000 kWh BESS, respectively, for the same time period. Fig. 6A shows a schematic of an example site as described with reference to the present invention. The PV panels and stationary BESS of the site may be sized according to the methods of Figs. 1 and 3. Fig. 6B shows example schematic of a site, such as the site of Fig. 6A, by way of a block diagram. Fig. 7 shows a flow diagram illustrating an example method of energy management for a site, such as the site of Figs. 6A to 6B. Fig. 8 shows an example graph illustrating operation of the method of Fig. 7 applied to a site, such as the site of Figs. 6A to 6B, as a function of time. Figs. 9A, 9C and 9D F show example graphs illustrating power usage of a site, such as the site of Figs. 6A and 6B, across a defined time period, 0 <t <1. Fig. 9B shows an example graph illustrating the state of charge of onsite energy storage systems of the site across the same defined time period. Fig. 10 shows a flow diagram of an example method of scheduling energy use on a site, such as the site of Figs. 6A to 6B. Fig. 11 shows a flow diagram of another example method of scheduling energy use on a site, such as the site of Figs. 6A to 6B. Fig. 12 shows a flow diagram of an example method of simulating energy management of a site, such as the site of Figs. 6A to 6B, to minimise power draw from the grid. Specific description Embodiments of the claims relate to methods of energy management and of sizing energy storage systems for installation on a site comprising a power generation source, in particular relating to a battery energy storage system. Fig. 1 shows a flow diagram illustrating an example method of sizing photovoltaic energy generation systems for installation on a site. Sizing refers to the process of determining the appropriate capacities of photovoltaic systems, energy storage systems, and / or other components to meet specific energy requirements. Proper sizing ensures efficient system operation and optimises system performance, ensures adequate power supply during peak demand, reduces system costs, and prolongs the lifespan of system components. The method comprises obtaining an indication of predicted power demand of the site (102). For example, by utilising historic demand side data, energy consumption for a site may be predicted as a function of time. A required capacity of power generation for the site may then be determined (104) based on the indication of predicted power demand of the site. Based on the required capacity of power generation, the method may further calculate a number of photovoltaic (PV) panels required to provide the required capacity of power generation. This calculation may be based on efficiency and performance data relating to specific PV panels, as well as environmental data, for example weather forecasting and historic weather data to estimate a realistic amount of energy generation for each PV panel. Finally, the method comprises constraining the number of PV panels by available area for installation at the site. This is advantageous to ensure that the calculated number of PV panels are able to be physically installed on the site due to space availability. The method may obtain or estimate the available area for installation of PV panels based on an image of the site, such as a satellite image, site plan, or map. For example, Fig. 2A shows an example satellite image 200 of a site. As shown in Fig. 2B, the method is configured to generate at least one closed polygonal chain based on the satellite image 200 to define an area. The polygonal chain may be defined by a plurality of geographical coordinates or grid references which form the vertices of the polygonal chain. Generating the polygonal chain may be achieved using artificial intelligence (Al) or machine learning (ML) methods, for example wherein the Al or ML is trained to identify an area based on a perimeter or boundary of a building, site, or area. In particular, the method may be trained to identify an area based on one of available roof area, available car park area, available lawn area, and so on. For example, as shown in Fig. 2B, area 202 indicates a first area of available roof space, suitable for installing PV panels. Areas 204, 206, and 208 also indicate respective areas of available roof space. As shown in Fig. 2B, an area of roof area in between area 202 and 206 which contains pipework has not been identified as available roof space because the pipework could interfere with installation of the PV panels. Area 210 indicates an area of available car parking space. This may be suitable for installation of PV panels, in particular for installation of PV canopies to be constructed over the car parking area such that car parking spaces are not lost. Area 212 indicates an area of available lawn or disused land, suitable for installation of PV panels. The method may then calculate the available area for installation of PV panels based on determining the area of the identified regions 202 to 212 from the image. The area within the polygonal chain may be calculated based on geographical coordinates or grid references of the vertices of the chain, or a known scale of the image. The number of PV panels able to be installed within this area can then be calculated. When constraining the number of PV panels by available area for installation at the site, the method may be configured to additionally consider the category of the area of the identified regions when calculating the available area for installation. For example, comparable areas of roof space and car park may not equate to the same number of PV panels being able to be installed in that space, for example the number of PV panels able to be installed in canopies over a car park area may differ compared to the number of conventional PV roof panels able to be installed on an equivalent roof area. This may be advantageous to ensure that the constrained number of photovoltaic panels is accurate. This method is particularly suited to installation of PV panels due to their scalable and modular design. Optionally, the method further comprises installing the constrained number of PV panels on site. The payback period of the constrained number of PV panels on site may be calculated. Firstly, initial estimates of the operational energy costs without PV (C_W0_PV) are calculated based on current energy tariffs for a period, for example annually. Secondly, net costs with PV (C_W_PV) are estimated based on current energy tariffs, and an estimate of the energy produced by the PV across the same time period, for example annually. Finally, the savings can be calculated based on (C_W0_PV) - (C_W_PV). Projections of net cost associated with PV usage to compute payback may account for degradation of PV panels using efficiency loss of the panels per year, for example accounting for a 0.5% reduction in efficiency per year. Projections of net cost with PV {C_W_PV) and without PV (C_W0_PV) may also account for energy tariff inflation per year, for example such as a 4.5% increase per year. Thus, using panel degradation, energy inflation, and PV generation for each year, the savings are calculated which are subtracted from the overall cost of installation of the PV system to calculate the payback period. The method may continue as shown in Fig. 3 to size energy storage systems for installation on a site. Firstly, the method obtains an indication of predicted surplus power generation by the power generation source (302). This may be obtained based on the constrained number of PV panels for the site. For example, based on the number of PV panels, the energy produced by the PV can be projected across a time period, preferably across a year. This projection may account for geographical location, and / or historical weather data, including sunlight hours, etc. Utilising again the demand side data to obtain energy consumption for the site, the method may calculate the amount of PV energy that would be consumed across the year, and the amount of PV energy that would be exported to the grid as surplus energy, for example during periods where PV energy generation is in excess of energy consumption for the site. For example, Fig. 4 shows a graph illustrating the energy trends over a typical work week and weekends for a site, including an extended weekend. The graph illustrates the predicted amount of PV generation 402, the predicted site energy consumption or demand 404, and the net generation 406. In this example, as shown in Fig. 4, the net generation 406 is calculated by the predicted site energy consumption or demand 404 minus the PV generation 402. As such, surplus energy is defined as the amount of energy when the net generation 406 on the graph is below 0 kWh. During these periods, surplus energy would ordinarily be exported to the grid in the absence of an energy storage system of the present invention. In the example shown in Fig. 4, peaks in surplus electricity can be shown to coincide with weekends when the site has reduced operation and thus reduced demand 404. The method then determines required capacity of an energy storage system based on the predicted surplus power (304). This may be done by applying net metering to obtain the ratio of consumption and export. Fig. 5A shows a graph 500 illustrating the relationship between the gradient of export ratio (Per Unit, PU, per megawatt hour, MWh) against energy storage capacity (megawatt hours, MWh). In this particular example, the energy storage capacity is defined as battery size, referring to the capacity of a battery energy storage system (BESS). This graph may be plotted based on the predicted surplus power which is used to determine the ratio of power which is exported to the grid for different energy storage capacities. For example, increasing the energy storage capacity of the system reduces the amount of power which is exported. Determining the required capacity of the energy storage system may be based on identifying a knee point of the curve shown in Fig. 5. Determining the required capacity of the energy storage system based on a knee point of export ratio against energy storage capacity may be advantageous to determine the optimum capacity required for a particular site by minimising the amount of surplus power generated by the power generation source which is offloaded to the grid without being utilised by the site, whilst also minimising the capacity of the system to avoid unnecessarily bulky and space-consuming systems on site, as well minimising cost. The knee point represents an inflection point where the benefit of increasing energy storage capacity is no longer increasing rapidly, and is no longer worth the further increases - a cutoff point of diminishing returns. Purely for illustrative purposes, Figs. 5B to 5E compare the simulated usage of a 1000kWh BESS and a 5000kWh BESS for the same predicted loading. Figs. 5B and 5D each show the same predicted PV generation of a site, represented by lines 512. The amount of energy exported to the grid when paired with a 1000kWh BESS is illustrated by export 514B in Fig. 5B. By contrast, the amount of energy exported to the grid when paired with a 5000kWh BESS is illustrated by export 514D in Fig. 5D. The amount of energy exported to the grid 514D is greatly reduced compared to the amount of energy 514B exported to the grid when paired with a 1000kWh BESS. This is because energy is exported to the grid in the event that the PV generation exceeds the BESS capacity. The BESS capacity is represented as a function of time for a 1000 kWh BESS in graph 520 of Fig. 5C. By contrast, the BESS capacity of a 5000 kWh BESS is shown as a function of time in graph 540 for the same time period in Fig. 5E. The amount of time that the 5000 kWh BESS is operating at full capacity is greatly reduced compared to the amount of time that the 1000 kWh BESS is operating at full capacity, as such the amount of energy exported 514D to the grid for a 5000 kWh system is greatly reduced compared to the amount of energy 514B exported to the grid for a 1000 kWh BESS system. Optionally, the method further comprises obtaining an indication of a predicted number of electric vehicles, EVs, on the site available for vehicle-to-everything, V2X, utilisation (306) and determining a usable capacity of the EVs on the site available for V2X utilisation, based on the predicted number of EVs (308). Determining the usable capacity of the EVs on the site available for V2X utilisation may be based on an indication of a predicted number of electric vehicles as a function of time, for example based on usage patterns of the EVs or the average time an EV may be available on the site for V2X utilisation. Usage patterns of the EVs may be determined by, for example, working shift patterns of the users or owners of the EVs. Determining a usable capacity of the EVs may also be based on the usable capacity of each available EV, for example based on the make and / or model of the EV if known. This may be appropriate, for example, where a fleet of known EVs are present on site. Where a usable capacity is not known, for example based on make and / or model, an average usable capacity may be assumed for a vehicle. It is noted that the determined usable capacity of the EVs may not directly correlate to the actual capacity of the EVs on the site available for V2X utilisation. For example, the actual capacity of the EVs may be greater than the determined usable capacity in order to account for factors which may reduce the usable capacity, such as state of charge window restrictions, performance inefficiencies, degradation, and / or aging of the EVs, for example. As such, usable capacity may be determined as a function of actual EV capacity and a state of charge window restriction. Purely for illustrative purposes, a state of charge window restriction may restrict the usable capacity of an EV from 100% to 30%, for example. A state of charge window restriction may be advantageous to ensure a minimum state of charge is maintained in the EVs. A state of charge window restriction may also be advantageous to improve longevity and battery health in the EVs available for V2X utilisation. The method then further comprises sizing an energy storage system capacity for installation based on (i) the required capacity of an energy storage system, and (ii) the usable capacity of the electric vehicles on the site available for V2X utilisation (310). The energy storage system capacity for installation may be determined based on the required capacity of an energy storage system minus the usable capacity of the electric vehicles on the site available for V2X utilisation. However, the skilled person will understand the required capacity of an energy storage system minus the usable capacity of the electric vehicles on the site available for V2X utilisation may not directly correlate to the actual capacity of the energy storage system required by the site for installation. For example, the actual capacity of the energy storage system may be greater in order to account for factors which may reduce the usable capacity of an energy storage system. For example, factors which may impact the usable capacity of an energy storage system may include state of charge window restrictions, performance inefficiencies, degradation, and / or aging of the energy storage system. As such, sizing the energy storage system capacity for installation may further comprise determining an actual required capacity for installation based on the required capacity of an energy storage system, and the usable capacity of the electric vehicles on the site available for V2X utilisation, wherein the determined required capacity is defined as the required usable capacity of the energy storage system. Usable capacity may be determined as a function of actual required capacity (e.g. BESS total capacity) and a state of charge window restriction. Purely for illustrative purposes, a state of charge window restriction may restrict the usable capacity of an energy storage system from 100% to 30%, for example. Additionally or instead, usable capacity may be determined as a function of actual required capacity (e.g. BESS total capacity) and a rate of capacity fade. The rate of capacity fade may be determined based on modelling the intended usage (e.g. charge and discharge cycles) of the BESS to determine a rate of degradation. The actual required capacity may then be adjusted based on the rate of capacity fade and a desired lifecycle. This may be advantageous to prolong the usable life of a BESS, based on the intended use and application. Optionally, the method further comprises installing an energy storage system, such as a BESS, on site according to the sized actual required capacity for the energy storage system. Whilst the example described above in relation to Fig. 3 discloses obtaining an indication of predicted surplus power generation based on the constrained number of PV panels for the site, the skilled person will understand that the predicted surplus power generation may additionally or alternatively be obtained and / or calculated based on alternative power generation sources, for example such as other renewable energy sources, such as wind turbines, ground source heat pumps or air source heat pumps, etc, or other non-renewable onsite power generation sources, such as generators etc, including biofuel generators. A schematic of an example site 600 suitable for use with embodiments of the present invention is shown for reference in Fig. 6A. The site 600 comprises a factory or building 602 which has a variable energy demand. The variable energy demand may be dictated by factors such as, but not limited to, heating, lighting, equipment usage, operation of a high voltage switchgear, etc. The site 600 may further comprise onsite PV panels 604, for example as sized and installed in accordance with the method of Fig. 1. The PV panels 604 are configured to generate electricity for use by the building 602. The site 600 may further comprise other renewable power sources, such as wind turbines 606, which are also configured to generate electricity and / or energy for use by the building 602. The site 600 further comprises a stationary battery energy storage system (BESS) 610, for example as sized and installed in accordance with the method of Fig. 3. The stationary BESS 610 is configured to store electrical energy generated by the power sources 604 and 606, and supply electricity for use by the building 602 as required. The site 600 further comprises a connection to the grid 608. The connection to the grid 608 is configured both to supply electricity for use by the building 602, and also to absorb excess electricity generated by the power sources 604 and 606 which is not used by the building 602 or stored by the stationary BESS 61O.The site 600 also comprises a plurality of EVs 612, such as an employee EV fleet, available for V2X utilisation. The plurality of EVs 612 are configured both to supply electricity for use by the building 602, and also to absorb excess electricity generated by the power sources 604 and 606 which is not used by the building 602 or stored by the stationary BESS 610. However, by nature of being EVs, the plurality of EVs 612 available for V2X utilisation by the site 600 is not static and may fluctuate based on whether the EVs are on site or in use. Optionally, the site 600 may further comprise supplementary energy storage, such as hot water tanks 614, configured to store nonelectrical energy, such as thermal energy in a hot water tank, and supply the non-electrical energy for use by the building 602 as required. Another example schematic of a site is shown by way of a block diagram in Fig. 6B. The block diagram 600B shows electrical couplings between the grid connection 608 and the demand side building 602 and V2X EV infrastructure 612. The block diagram 600B also shows electrical couplings between the onsite PV panels 604, and other onsite renewable sources 606, and the grid 608 and demand side building 602 and V2X EV infrastructure 612. This enables the onsite PV panels 604, and other onsite renewable sources 606, to power the demand side building 602, charge the V2X EV infrastructure 612, and offload surplus power generation to the grid 608. The electrical couplings are also coupled to a LCL filter 620 and inverter 622. A BESS DC-DC converter 624 is coupled between the inverter 622 and the stationary BESS 610. The inverter 622 is preferably a 3-phase grid-connected inverter configured to convert DC power from a source, such as a BESS 610 or solar panel 604, into AC power that can be fed into the electric grid 608 or demand-side load. The BESS 610 is therefore configured to store energy generated by the PV panels 604, and other onsite renewable sources 606 as charge, and power the demand side building 602 by discharging on demand. Optionally, the BESS 610 is also configured to export power to the grid 608. For example, the grid 608 may be configured to draw power from the BESS 610 during periods of high demand at the grid 608, in particular during periods of high demand at the grid 608 when the rate of power generation at the site, or SOC of the BESS 610, is greater than the site demand. This may be advantageous to provide stability to the grid 608 by enabling it to cope with high peak demand. The controller may also be configured for energy arbitrage, for example to sell power to the grid 608 at times of high grid demand, and / or to import power from the grid 608 at times of low grid demand. To control the active power (P) output of the inverter 622, a DQ-control (also known as synchronous reference frame control) algorithm is used. The DQ algorithm transforms the three-phase AC voltage and current signals into a rotating reference frame called the DQ frame. The D axis is aligned with the grid voltage, and the Q axis is perpendicular to it. The active power output of the inverter 622 is controlled by adjusting the amplitude of the D-axis current reference in the DQ frame. Active power is controlled by adjusting inverter phase angle. Lead deliver, lag consume. P oc 0 lp-vqiq-2(yM To control the reactive power (Q) output of the inverter, the amplitude of the Q-axis current reference is adjusted. This is done by adjusting the inverter's output voltage magnitude. (Vinverter ^grid) 17 vgrid 2 -^Q-Vgld A method of simulating energy management of a site comprising a power generation source and a battery energy storage system, such as site 600 of Fig. 6A or site 600B of Fig. 6B, to minimise power draw from the grid is shown in Fig. 12. The method comprises obtaining an indication of predicted power demand of a site across a first time period (1202); obtaining an indication of predicted local power generation across the first time period (1204); and predicting the rate of power draw from the grid across the first time period (1206) based on the predicted power demand and predicting rate of power generation. Predicting the rate of power draw from the grid across the first time period may comprise simulating the amount of active power and reactive power required across the first time period (1208) based on the predicted power demand and predicting rate of power generation and simulating a state of charge of the battery energy storage system across the first time period based on the determined power demand and determined rate of power generation, wherein simulating the state of charge of the battery energy storage system comprises simulating drawing at least a portion of reactive power from the battery energy storage system to reduce the apparent power draw from the grid (1210). Fig. 7 shows a flow chart illustrating a method 700 of energy management for a site, such as site 600 of Fig. 6A. As above, the site comprises at least one power generation source, such as PV panels 604 and / or wind turbines 606, and a battery energy storage system (BESS). As shown in Fig. 6A, the BESS may comprise a stationary battery unit 610, and at least one electric vehicle 612 on the site available for V2X utilisation. The system further comprises a controller (not shown), wherein the controller is configured to perform the method 700 of Fig. 7. The method 700 comprises obtaining a state of charge (SOC) of the BESS (702). The method 700 also comprises determining the real-time rate of power generation by the power generation source, such as the PV panels 604 and wind turbines 606 (704), and determining the real-time rate of power demand by the site 600. The real-time rate of power demand by the site 600 may be determined based on the heating, lighting, equipment usage, and / or operation of a high voltage switchgear, etc, of the building 602. The controller then determines whether the rate of power generation is higher than the rate of power demand (706). If so, the controller then determines if the state of charge of the BESS is below an upper threshold (708). If the state of charge of the BESS is below an upper threshold, the controller sends a control signal to charge the BESS using excess power generated by the PV panels 604 and wind turbines 606 (710), wherein the excess power generated is the remaining power in excess of the power demand. If the excess power generation is greater than a maximum battery charge rate, the controller sends a control signal to charge the BESS at its maximum charge rate, and export the remaining surplus energy in excess of the maximum charge rate. Otherwise, if the BESS SOC is below the upper threshold and the excess power generation is greater than the maximum battery charge rate, the controller sends a control signal to charge the BESS with the excess power generation. In the event that the rate of power generation is higher than the rate of power demand, and the state of charge of the BESS is equal to or above the upper threshold, the controller sends a control signal to export the surplus energy (712), wherein surplus energy is the remaining power in excess of the power demand of the site. In the event that the rate of power generation is lower than the rate of power demand, the controller then determines if the state of charge of the BESS is above a lower threshold (714). If the state of charge of the BESS is above a lower threshold, the controller sends a control signal to discharge the BESS at a discharge rate, wherein the discharge rate is based on the difference between the rate of power generation and the rate of power demand (716). If the difference between the load and generation is greater than the maximum battery discharge rate, the controller sends a control signal to discharge the BESS at its maximum rate and import the remaining energy imbalance from the grid 608. Otherwise, the controller is configured to keep the BESS state of charge unchanged if the rate of power generation lower than the power demand and the BESS is at or below its minimum state of charge (lower threshold). In the event that the rate of power generation is lower than the rate of power demand, and the state of charge of the BESS 610 is equal to or below the lower threshold, the controller sends a control signal to import the remaining energy imbalance from the grid 608, wherein the remaining energy imbalance is the remaining power demand at the site 600 in excess of the power generation. The controller is configured to keep the battery SOC unchanged if the battery SOC is equal to or above the upper threshold (the maximum state of charge threshold). The upper threshold and / or lower threshold may be different for the stationary battery unit 610 and the at least one electric vehicle 612, for example wherein the stationary battery unit 610 and EVs 612 are subject to different state of charge window restrictions. The upper threshold and / or lower threshold for the EVs 612 may vary as a function of time. For example, the lower threshold (minimum state of charge) may be higher closer to the end of the EV user’s shift to ensure the vehicle has enough charge for a journey, for example to transport the user home. Purely for illustrative purposes, the lower threshold for the EVs 612 may be 30% SOC before 3pm, and 50% SOC after 3pm. The maximum charge rate and / or maximum discharge rate for the stationary battery unit 610, and the at least one electric vehicle 612 may also be different, for example to preserve battery health and reduce degradation and aging. In use, the controller may prioritise discharging (and / or charging) of the EVs available for V2X through the V2X infrastructure 612, rather than the stationary battery unit 610. Once discharging of the EVs 612 reaches the lower threshold (minimum state of charge), the controller sends a signal to begin discharge of the stationary battery 610. Fig. 8 shows an example graph illustrating operation of the method of Fig. 7 applied to a site, such as the site 600 of Fig. 6A, across a time period, t. The area 802 represents the energy consumption (or demand, or load) of the site 600 which is variable over time. The area 804 illustrates the amount of renewable power generation 804, for example generated by the PV panels 604 and optionally the wind turbines 606. The area 806 illustrates a portion of the renewable power generation 804 which is used to charge the BESS, for example including EVs available for V2X 612 and the stationary battery 610, until the maximum state of charge (upper threshold) of each component in the BESS is reached. Charging the BESS only occurs when the amount of energy generation exceeds the energy demand of the site. The area 808 illustrates a portion of the energy demand that is provided by discharging the BESS. Discharging the BESS only occurs when the energy demand exceeds the amount of power generation. As above, the controller may prioritise discharging (and / or charging) of the EVs 612 available for V2X, rather than the stationary battery unit 610. Once discharging of the EVs 612 reaches the lower threshold (minimum state of charge), the controller sends a signal to begin discharge of the stationary battery 610. Figs. 9A to 9D show example graphs illustrating power usage of a site, such as the site 600 of Fig. 6A, and the state of charge of onsite energy storage systems, across a defined time period, 0 <t <1. Fig. 9A shows an example graph illustrating power demand and renewable power generation of a site, such as the site 600 of Fig. 6A, across the time period, t. The line 802 represents the energy demand of the site 600 which is variable over time. The line 804 illustrates the amount of renewable power generation 804, for example generated by the PV panels 604. Due to the nature of PV power generation and its reliance on solar energy, the power generation 804 is not tracked to the power demand 802. When the power demand 802 is greater than the power generation 804, there is a power generation deficit meaning that power must be drawn from (i) the grid 608, (ii) an onsite energy storage system, such as a stationary BESS 610 or V2X system 612, or (iii) an onsite generator. Fig. 9B shows the state of charge (SOC) of onsite energy storage systems. The line 902 represents the state of charge of a V2X system 612 comprising a plurality of EVs available for V2X utilisation. The line 904 represents the state of charge of a stationary BESS 610. At the beginning of the time period, time =0, the state of charge of the V2X system 612 is 80% and the SOC of the BESS 610 is 100%. As the power demand 802 is greater than the power generation 804, the controller is configured to draw power from the V2X system 612 to power the site load. As such, the V2X system is discharged. In this example, the V2X system is subject to a minimum state of charge threshold of 0.5, or 50%. As such, when the state of charge of the V2X system reaches the minimum SOC threshold, at approximately time =0.2, the controller is configured to instead draw power from the BESS 610 to power the site load, causing the BESS 610 to discharge. In this example, the controller is configured to prioritise discharging the V2X system 612 relative to the BESS 610. At approximately time =0.25, the power generation 804 equals the power load 802. As such, the controller is configured to stop power draw from the onsite energy storage systems and is instead powered by the PV power generation 804. From 0.25 <time <0.7, the PV power generation 804 exceeds the power load 802. During this period, the power load 802 is provided by the PV power generation 804. The controller is configured to use the excess power generation to also charge the V2X system 612 to restore the V2X system 612 to the original state of charge, in this case 0.8, or 80%, as shown by line 902. Optionally, the controller is configured to use the excess power generation to charge the V2X system 612 beyond the original state of charge, to an upper state of charge threshold. In this example, the upper state of charge threshold is 10% in excess of the original state of charge, in this case 0.9, or 90%. This may be advantageous to incentivise and improve participation of EV owners in V2X systems 612. Once the state of charge of the V2X system has been charged to at least the original state of charge, the controller is also configured to use the excess renewable power generation to charge the BESS 610, as shown by line 904, to a maximum state of charge threshold, in this case 1 or 100%. In the event that both the V2X system 612 and the BESS 610 are charged to their respective upper state of charge threshold, excess renewable energy generation 804 may then be exported to the grid 608 by the controller. This is shown in Fig. 9C wherein the line 906 represents power drawn from or exported to the grid 608. In this example, power >0 kW indicates power exported to the grid 608, whereas power <0 kW indicates power imported from the grid 608. For illustrative purposes, line 908 of Fig. 9C represents power generated by an onsite generator, such as a diesel or biofuel generator. Power <0 kW indicates power generated by the generator. As shown in Fig. 9C, the generator is activated from approximately time 0.1 to 0.15. The controller is configured to use power generated by the generator to power the load 802. As such, power generated by the generator 908 can supplement the power drawn from the onsite energy storage systems and reduce the rate of discharge. This can be seen in Fig. 9B where the rate of discharge of the V2X system 612, indicated by the gradient of line 902, is shown to reduce from time 0.1 to 0.15, consistent with power generation 908 from the generator. From time 0.7 onwards, power demand 802 increases above renewable power generation 804. As such, the controller is configured to once again control the onsite energy storage systems to discharge to power the load. However, by contrast to the earlier discharge-charge cycle, the controller is configured to prioritise discharging the stationary BESS 610 in preference to discharging the V2X system. This discharge preference may be subject to time constraints, for example for a first time period, for example from 0 <time <0.6, the controller may be configured to prioritise discharging the V2X system, whereas for a second time period, such as time >0.6, the controller may be configured to prioritise discharging the BESS 610. This may be advantageous to ensure that the V2X system 612 is sufficiently charged for use, for example ensuring that EVs within the V2X system are sufficiently charged at the end of a worker’s shift to allow the EV to be driven home. Alternatively, the discharge preference may be subject to charging cycle constraints, for example for a first discharge cycle within a time period, the controller may be configured to prioritise discharging the V2X system, whereas once the V2X system has been recharged within the time period, the controller may be configured to prioritise discharging the BESS 610 for any subsequent discharging cycles within the time period. The power draw from the onsite energy storage systems across the same time period is summarised in Fig. 9D. The line 910 represents the power supply and draw from the V2X system 910. The line 912 represents the power supply and draw from the BESS 610. Power of the scale less than 0 kW indicates power drawn by the onsite energy storage systems for charging, whereas power of the scale greater than 0 kW indicates power supplied by the onsite energy storage systems (i.e. whilst discharging). Fig. 10 shows a flow diagram of an example method 1000 of scheduling energy use on a site comprising a power generation source and a battery energy storage system, such as site 600 of Fig. 6A. The method comprises first obtaining an indication of predicted power demand by the site over a first time period (1002), and obtaining an indication of a predicted rate of power generation by the power generation source, such as the PV panels 604, over the first time period (1004). In some examples, the controller may be further configured to generate a prediction of power demand by the site over the first time period, and / or predict the rate of power generation by the power generation source over the first time period itself. The method also comprises predicting the state of charge of a battery energy storage system (1006), such as stationary BESS 610 and / or V2X system 612. The prediction of the state of charge may be determined by simulating the usage of the battery energy storage system, for example according to the method 700 of Fig. 7. For example, the battery energy storage system may be configured to (i) charge the battery energy storage system using excess power generated by the power generation source if the state of charge of the battery energy storage system is below an upper threshold, wherein the excess power generated is the remaining power in excess of the power demand; and (ii) discharge the battery energy storage system if the state of charge of the battery energy storage system is above a lower threshold and in the event that the rate of power generation is lower than the rate of power demand. The method further comprises identifying at least one subset of the firm time period (referred to as “a second time period”) wherein the state of charge of the battery is above a mid-threshold (1008), the mid threshold being above the lower threshold, and equal to or less than the upper threshold. The controller is then configured to schedule operation of at least one piece of equipment on the site during the at least one second time period (1010). Preferably, the method comprises rescheduling operation of at least one piece of equipment from a time period wherein the power demand is greater than the rate of renewable power generation, to the second time period. The second time period may be a time period wherein the rate of power generation is predicted to be greater than the predicted power demand. Scheduling operation of equipment in this manner may be advantageous to reduce peaks in demand and thereby reduce energy draw from and reliance on the grid. For example, scheduling operation of the equipment during the second period may facilitate operation of the equipment to be powered by at least one of (i) renewable power generation, for example by PV solar panels, and (ii) onsite energy storage, such as a BESS system. Based on the scheduled operation of the equipment, the method may be configured to update the indication of predicted power demand of the site across the first time period. This may be advantageous because scheduling of the equipment may change the distribution of predicted power demand, for example by lessening peaks in demand and resulting in a more evenly spread load. In some examples, the method 100 of Fig. 1 may be configured to be rerun based on the updated indication of predicted power demand to recalculate the constrained number of PV panels required. As such, scheduling for demand-side shifting is preferably performed before installation of the PV panels. This may be advantageous as the updated indication of predicted demand, based on the scheduling of equipment, may reduce the required capacity of power generation for the site due to reduced peak demand, and thus reduce the number of photovoltaic panels required. In addition, or alternatively, the method 300 of Fig. 3 may also be configured to be run (or rerun) based on the updated indication of predicted power demand which in turn results in an updated indication of predicted surplus power generation by the power generation source. In examples where the method 100 of Fig. 1 and the method 300 of Fig. 3 are both rerun, the indication of predicted surplus power generation (302) will also be updated based on the updated number of required solar panels for installation. As such, scheduling for demand-side shifting is preferably performed before installation of the BESS, and optionally the PV panels. This may be advantageous as the updated indication of predicted demand, based on the scheduling of equipment, may reduce the required capacity of energy storage for the site due to reduced peak demand, and thus reduce the capacity of the stationary BESS required. Optionally, the method further comprises scheduling discharge of the battery energy storage system during the at least one second time period, such that discharge of the battery is configured to at least partially power operation of the at least one piece of equipment. Alternatively, operation of the scheduled piece of equipment may be configured to be powered by excess power generation. Fig. 11 illustrates a method 1100 of scheduling energy storage on a site comprising a power generation source and a battery energy storage system, such as site 600 of Fig. 6A, the method 1100 comprising obtaining an indication of a predicted power demand by the site over a first time period (1102), obtaining an indication of a predicted rate of power generation by the power generation source over the first time period (1104); and obtaining an indication of a predicted the state of charge of the battery energy storage system (1106). Obtaining an indication of a predicted the state of charge of the battery energy storage system may be achieved by simulation. For example, the state of charge of the battery energy storage system may be simulated according to the method 700 of Fig. 7, for example wherein the battery energy storage system is configured to (i) charge the battery energy storage system using power generated by the power generation source wherein the excess power generated is the remaining power in excess of the power demand; and (ii) discharge the battery energy storage system in the event that the rate of power generation is lower than the rate of power demand. The method 1100 further comprises identifying at least one second time period wherein the rate of power generation is predicted to be greater than the predicted power demand, and the state of charge of the battery is below a threshold, wherein the second time period is a subset of the first time period (1108), and scheduling charging of the battery energy storage system during the at least one second time period (1110). The controller is then configured to implement charging of the battery energy storage system according to the scheduling (1112), such that the battery energy storage system is charged during the second time period. It will be appreciated from the discussion above that the embodiments shown in the Figures are merely exemplary, and include features which may be generalised, removed or replaced as described herein and as set out in the claims. In the context of the present disclosure other examples and variations of the apparatus and methods described herein will be apparent to a person of skill in the art.

Claims

1. A method of sizing energy storage systems for installation on a site comprising a power generation source, the method comprising:obtaining an indication of predicted surplus power generation by the power generation source;determining a required capacity of an energy storage system based on the predicted surplus power generation to minimise energy export to grid;obtaining an indication of predicted number of electric vehicles on the site available for vehicle-to-everything, V2X, utilisation;determining a usable capacity of the electric vehicles on the site available for V2X utilisation, wherein the usable capacity of the electric vehicles is based on the number of electric vehicles predicted to be on the site, the total capacity of each electric vehicle, and an electric vehicle state of charge window restriction; andsizing an energy storage system capacity for installation based on (i) the required capacity of an energy storage system, and (ii) the usable capacity of the electric vehicles on the site available for V2X utilisation.

2. The method of claim 1 further comprising installing the energy storage system on the site, according to the determined size of the energy storage system capacity.

3. The method of any preceding claim, wherein sizing the energy storage system capacity comprises calculating the required capacity of an energy storage system minus the usable capacity of the electric vehicles on the site available for V2X utilisation.

4. The method of any preceding claim further comprising:determining a usable capacity of an energy storage system, wherein the usable capacity is based on the total capacity of an energy storage system and a state of charge window restriction;wherein sizing the energy storage system capacity is further based on the usable capacity of an energy storage system.

5. The method of any preceding claim, wherein the energy storage system is a batteryenergy storage system.

6. The method of claim 5 further comprising:modelling the degradation of the battery energy storage system;determining capacity fade of the battery energy storage system based on the modelled degradation; andwherein sizing the energy storage system for installation is further based on the determined capacity fade of the battery energy storage system.

7. The method of any preceding claim wherein obtaining the indication of predicted surplus power generation comprises predicting the amount of surplus power generation generated by the power generation source, optionally wherein the power generation source is a renewable power generation source.

8. The method of claim 7, further comprising:obtaining an indication of predicted power demand of the site;determining a required capacity of power generation for the site, based on the indication of predicted power demand of the site;calculating a number of photovoltaic panels required based on the required capacity of power generation; andconstraining the number of photovoltaic panels by available area for installation at the site;wherein obtaining an indication of predicted surplus power generation by the power generation source is based on the constrained number of photovoltaic panels.

9. The method of claim 8 further comprising installing the constrained number of photovoltaic panels.

10. The method of any preceding claim wherein obtaining the indication of predicted surplus power generation by the power generation source comprises:obtaining an indication of predicted power demand of the site over a first time period;predicting rate of power generation by the power generation source over the firsttime period;identifying at least one second time period wherein the predicted rate of power generation is greater than the predicted power demand, wherein the second time period is a subset of the first time period;scheduling operation of at least one piece of equipment on the site during the at least one second time period;updating the indication of predicted power demand of the site over the first time period based on the scheduling; andcalculating an indication of predicted surplus power generation by the power generation source, based on the updated indication of predicted power demand and the predicting rate of power generation.

11. A method of sizing photovoltaic energy generation systems for installation on a site, the method comprisingobtaining an indication of predicted power demand of the site;determining a required capacity of power generation for the site, based on the indication of predicted power demand of the site;calculating a number of photovoltaic panels required based on the required capacity of power generation; andconstraining the number of photovoltaic panels by available area for installation at the site12. The method of claim 11 further comprising installing the constrained number of photovoltaic panels.

13. A method of energy management for a site comprising a power generation sourceand a battery energy storage system, the method comprising:obtaining a state of charge of the battery energy storage system;determining the rate of power generation by the power generation source;determining the rate of power demand by the site; and:in the event that the rate of power generation is higher than the rate of power demand:if the state of charge of the battery energy storage system is below an upper threshold, charging the battery energy storage system using excess power generated bythe power generation source, wherein the excess power generated is the remaining power in excess of the power demand; andin the event that the rate of power generation is lower than the rate of power demand: if the state of charge of the battery energy storage system is above a lower threshold, discharging the battery energy storage system at a discharge rate, wherein the discharge rate is based on the difference between the rate of power generation and the rate of power demand.

14. The method of claim 13 wherein charging the battery energy storage system comprises charging the battery energy storage system at a maximum charge rate.

15. The method of claim 14 further comprising, determining that the rate of excess power generation is greater than the maximum charge rate, and exporting surplus energy, wherein surplus energy is the remaining power in excess of the power demand of the site, and the maximum charge rate of the battery energy storage system.

16. The method of any of claims 13 to 14 wherein, in the event that the difference between the rate of power generation and the rate of power demand is greater than a maximum discharge rate, the method further comprises importing the remaining energy imbalance from the grid, wherein the remaining energy imbalance is the remaining power demand in excess of power generation and the maximum discharge rate.

17. The method of any of claims 13 to 16 wherein, in the event that the rate of power generation is lower than the rate of power demand, and if the state of charge of the battery energy storage system is equal to or below the lower threshold, importing the remaining energy imbalance from the grid, wherein the remaining energy imbalance is the remaining power demand in excess of the power generation.

18. The method of any claims 13 to 17 wherein, in the event that the rate of power generation is higher than the rate of power demand, and if the state of charge of the battery energy storage system is equal to or above the upper threshold, exporting surplus energy, wherein surplus energy is the remaining power in excess of the power demand of the site.

19. The method of any claims 13 to 18 wherein the battery energy storage system comprises at least one stationary battery unit, and at least one electric vehicle on the site available for vehicle-to-everything, V2X, utilisation.

20. The method of claim 19 wherein the upper threshold is different for the at least one stationary battery unit and the at least one electric vehicle; and / orthe lower threshold is different for the at least one stationary battery unit and the at least one electric vehicle.

21. The method of any claims 19 to 20 wherein the battery energy storage system comprises a maximum charge rate, wherein the maximum charge rate for the at least one stationary battery unit, and the at least one electric vehicle are different.

22. The method of any claims 19 to 21 wherein the battery energy storage system comprises a maximum discharge rate, wherein the maximum discharge rate for the at least one stationary battery unit, and the at least one electric vehicle are different.

23. A method of scheduling energy use on a site comprising a power generation source and a battery energy storage system, the method comprising:predicting power demand by the site over a first time period;predicting rate of power generation by the power generation source over the first time period;predicting the state of charge of the battery energy storage system, wherein the battery energy storage system is configured to:(i) charge the battery energy storage system using excess power generated by the power generation source if the state of charge of the battery energy storage system is below an upper threshold, wherein the excess power generated is the remaining power in excess of the power demand; and(ii) discharge the battery energy storage system if the state of charge of the battery energy storage system is above a lower threshold and in the event that the rate of power generation is lower than the rate of power demand;identifying at least one second time period wherein the state of charge of the battery is above a mid threshold, wherein the second time period is a subset of the first time period,and wherein the mid threshold is above the lower threshold, and equal to or less than the upper threshold;scheduling operation of at least one piece of equipment on the site during the at least one second time period; andscheduling discharge of the battery energy storage system during the at least one second time period, such that discharge of the battery is configured to at least partially power operation of the at least one piece of equipment.

24. The method of claim 23 wherein the at least one second time period further comprises wherein the rate of power generation is predicted to be greater than the predicted power demand.

25. A method of scheduling energy storage on a site comprising a power generation source and a battery energy storage system, the method comprising:predicting power demand by the site over a first time period;predicting rate of power generation by the power generation source over the first time period;predicting the state of charge of the battery energy storage system, wherein the battery energy storage system is configured to:(i) charge the battery energy storage system using power generated by the power generation source wherein the excess power generated is the remaining power in excess of the power demand; and(ii) discharge the battery energy storage system in the event that the rate of power generation is lower than the rate of power demand;identifying at least one second time period wherein the rate of power generation is predicted to be greater than the predicted power demand, and the state of charge of the battery is below a threshold, wherein the second time period is a subset of the first time period;scheduling charging of the battery energy storage system during the at least one second time period; andimplementing charging of the battery energy storage system according to the scheduling, such that the battery energy storage system is charged during the second time period.

26. A method for energy management of a site comprising a power generation source and a battery energy storage system to minimise power draw from the grid, the method comprising:predicting power demand of the site across a first time period;predicting rate of power generation by the power generation source across the first time period;predicting the rate of power draw from the grid across the first time period, based on the predicted power demand and predicting rate of power generation;simulating the amount of active power and reactive power required across the first time period, based on the predicted power demand and predicting rate of power generation;simulating a state of charge of the battery energy storage system across the first time period based on the determined power demand and determined rate of power generation, wherein simulating the state of charge of the battery energy storage system comprises simulating drawing at least a portion of reactive power from the battery energy storage system to reduce the apparent power draw from the grid.

27. The method of claim 26 comprising controlling the charge and / or discharge of the battery energy storage system based on the simulated state of charge of the battery energy storage system to reduce the to reduce the apparent power draw from the grid.

28. The method of claim 27 wherein controlling the charge and / or discharge of the battery energy storage system comprises scheduling charge and / or discharge of the battery energy storage system across the first time period based on the simulated state of charge of the battery energy storage system to reduce the to reduce the apparent power draw from the grid.

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