A Study on Applicability of Distributed Energy Generation, Storage and Consumption within Small Scale Facilities
Abstract
:1. Introduction
1.1. A perspective of Distributed Energy Generation and Resources
1.2. Economic Approach to Distributed Energy Generation and Resourcers
1.3. Contributions of the Manuscript
1.4. Paper Structure
2. Related Works
2.1. A Systematic Survey of Business Models for Smart Micro-Grids
2.2. Business Interactions Modeling for Systems of Systems Engineering
2.3. Smart Grid Solutions, Services, and Business Models Focused on Telco
2.4. Other Related Works
2.5. Open Issues and Challenges
- Unclear business plans. While several hints are offered about how to create business from the starting point that either scattered microgrids or the Smart Grid could provide, this idea is never addressed with enough level of detail, nor descriptions where key partners, activities or channels are shown.
- Unclear value propositions. As it happened previously, it is hard to identify the services that would be offered under those new business models that would create the competitive advantages or the profitability for third party companies, even though there is a clear potential for them to happen.
- Inadaptability to new power infrastructures beyond the mere description of what prosumers can offer. Despite having thorough descriptions of what can be used by consumers that are turned into prosumers, studies on how to enable to possibility for them to have a profit with accurate figures (source of the revenues, breaking points, etc.) are missing.
- Inability to provide any value to regular, current customers of the power grid. Regular customers that use electricity in their everyday activities are not given a chance to have a better way to use their surplus, unused energy.
3. Business Models for Dual Profitability
- The produced electricity can either be purchased by an electricity commercializing company (especially if renewable energies are taken into account) or acquired via auctions. In this latter way, power generating companies offer their product (electricity) at a particular price based on an estimation on how much energy will be consumed during a certain period of time (being the next day one of the most usual ones). An electric pool is the location used by large companies to trade with the energy they provide. It is in this pool where electricity generating companies will set a price for the energy that they have produced.
- A different kind of companies, namely the power commercializing ones, must apply for the electricity offered by the producer ones according to how much they estimate they will be able to sell during the set period of time. Commonly, applications must be aimed to national entities that, in the European Union, are the Nominated Electricity Market Operator (NEMO), aimed to regulate single day-ahead and/or intraday coupling in the electricity market.
- The NEMO will establish the offered prices of the electricity that is provided by the power producing companies up until all the demand estimated from the commercializing companies is satisfied. Commonly, the NEMO will start assigning power supplies by choosing the cheapest offer done by the power producing companies, and finish with the most expensive ones, taking into account the estimations done by the power commercializing companies. The resulting price when finishing the auction will be the one to be charged to the end customers, provided that power is bought for the timespan that has been agreed in the auction. As it can be inferred, consumption forecasting will have a decisive role in fixing electricity pricing.
- This final price has different names that depend on the location of the energy market. For instance, in Spain is called Voluntary Price for the Small Consumer (Precio Voluntario de Pequeño Consumidor, PVPC [11]).
- Variability of the electric tariff during a timespan. In most of the cases, the price of the electricity has some variation during the 24 h of a day, regardless of how insignificant it may look like at first sight. Therefore, the cost of the energy consumed by a collection of loads may be controlled to an extent if mechanisms to optimize their electricity usage are implemented. For example, as it can be seen in Figure 1, electricity cost per kWh can greatly vary during a specific period of time, with a group of hours when energy cost is comparatively cheap due to the low demand of electricity (off-peak hours) and other period of time when price increases due to the higher demand (peak hours). In this figure, three different tariffs have been established: one default tariff with no particular incentives to consume electricity during certain periods of time, described in Figure 1 as Tarifa por defecto (Peaje 2.0 A) and represented with a red line; another one that clearly defines two periods to improve consumption efficiency, represented in Figure 1 with a blue line and named Eficiencia 2 períodos (Peaje 2.0 DHA); and a third one used for Electric Vehicles (EVs) with minor differences when compared to the one that has two different pricing zones, named Vehículo Eléctrico (Peaje 2.0 DHS) in Figure 1 and represented with a green line. Even when tariffs are flatter (default tariff in Figure 1) there is still some variation that can be used to the end user´s advantage. There are several parameters that must be taken into account when dealing with electricity costs, such as the conditions used to supply electricity (small consumer pricing policies, active energy billing, energy cost, access toll, etc.). Again, it must be born in mind that the values and parameters described are done so according to national legislation, where energy and electricity pricing are part of a regulated sector. Consequently, electricity tariffing procedures may vary greatly from one country or location to another.With this price variation in mind, as well as how much electricity will be consumed by the end user, the supply contract that will be signed to charge energy usage of this end user should be strongly based on the consumption habits that they have (hence considering the amount of loads and how they are used in their dwell or facility).
- Infrastructure availability to store energy. If energy storage capability is available, then electric energy can be saved if transformed into chemical energy into an “energy reservoir” for its most optimal usage, thus decreasing energy costs when energy might be most demanded and therefore its price is higher. What is more, trading strategies can be enabled for small consumers willing to use their stored energy as a resource in the market, since power can be acquired, stored and sold as any other commodity, with the difference that power availability is widespread and an instance of these storage technologies can be used by any person that is supplied electricity and is willing to save it during a specific period of time. Even though it is a technology that has yet to become fully mature, home batteries are gaining increasing success, with development works being done in their most prominent electric features, such as output current ripple [13]. Other solutions rely on designing power units to be installed on vehicles that could be used as power suppliers [14]. The most important home batteries to be known at the time of writing this manuscript are:
- Tesla Powerwall. This the most popular home battery available in the market, as it is regarded as the forerunner of all the other related technologies that, to an extent, mimic the idea and design of this solution. The capabilities that are provided by Tesla Powerwall are above the average of what is provided by the other manufacturers. By the time of writing this manuscript, it was claimed by Tesla Motors that this home battery was sold out until mid-2016 [15].
- Panasonic LJ-SK84A. This battery is sold with the same concept as tesla Powerwall, and its capabilities and features are comparable to the ones offered by the former, according to the specifications displayed by the Panasonic model. This solution is mainly focused on the Smart Grid, as it is conceived and commercialized to an extent as a hardware element to make use of the energy obtained from solar panels [16].
- Powervault models (Lithium ion and lead acid). This company is a startup based in London that offers a myriad of products oriented to energy storage. According to [17], the main business lines are focused on the commercialization of either Lithium-ion batteries with a longer lifespan, or lead acid ones with better performance but shorter life. Unlike Panasonic and Tesla, the models that are sold by this company are the bulk of their business, rather than another branch of a broad range of activities. This is something to be taken into account overall as far as the applicability of the usage of batteries is concerned, since profit in this company regarding this activity is way more critical than in many of the other companies. Powervault battery models are usually regarded as more compact ones than the previous ones; therefore, their dimensions and energy availability are smaller.
- Orison (Tower and panel models). This is another solely bent on the home batteries business. As it happened with Powervault, battery models offered by this company are overall smaller than the former ones in terms of available power, energy and dimensions. They make use of a home-friendly design [18], to the point that they are sold with an appearance that is either on towers and panels used for interior design purposes in order to enhance the ornamental possibilities of these devices.
- Mercedes Benz Power Pack. The concept of the batteries manufactured here is different than the ones shown before, since they are sold as separated units that offer a comparatively low amount of power, but they can be combined to offer up to 20 kWh of energy [19]. While other manufacturers offer the same possibility (for example, Tesla Powerwall models can be combined to offer up to 90 kWh of energy) they seem lighter and easier to handle than the other models. By the time of writing this manuscript, there was little public information available regarding their major specifications.
- BYD Mini ES. This manufacturer offers a plethora of solutions focused on energy storage for homes, offices and large-scale businesses [20]. Their solutions are sold under a modular concept, although the modules are conceived for industrial purposes, so they are larger and offer more capabilities than the other ones that have been conceived for a more domestic use. For the purpose of this manuscript, the Mini ES model has been chosen to be mentioned, since its dimensions and weight are roughly match the overall dimensions offered by other batteries. Overall, the capabilities that it offers are relatively high regarding the relation between its dimensions and available energy, but its weight is comparatively higher too.
- Nissan xStorage. This product was yet to start being commercialized by the time this manuscript was written. Consequently, there was little information available regarding this home battery solution; it was expected that once it starts begin commercialized, specifications will be offered [21].
- sonnenBatterie eco. This German manufacturer of batteries commercializes several models that, depending on their applicability, can be applied to a home environment or a company; taking into account the scope of this manuscript it has been decided to include in this study the home model called eco. It is strongly focused on working in cooperation with solar energy, as the web site where it is shown describes how energy stored from the one produced during Sun hours can be put to a use when there is an absence of sunshine [22]. Overall capabilities are within the range established by all the previously shown models.
3.1. Business Models Proposal
- Storage solutions. Energy storage solutions may be provided if the end user has no infrastructure available to begin with.
- Trading capabilities. An algorithm must be used to trade with the energy that has been stored at the optimal moment and with the optimal partner in order to maximize the profits derived from that activity.
- Interconnectivity. How to interconnect different energy storage facilities so that energy can be transferred from one location to another or to provide electricity to a cluster of users must be figured out when several of them are gathered under the same third party.
- Ascending energy storage: Power is gradually stored in the home battery. Rather than invariably increasing the power storage for a while, what matters is the tendency: it might be convenient in a particular lapse of time to discharge the battery (for instance, due to better prices in the markets), but the overall tendency will be increasing power storage. However, energy cannot be held during an indefinite amount of time because the home battery can only store a limited amount of energy. Even if the end user was able to extend power storage, saving it during an indefinite amount of time is rarely the most profitable option.
- Constant energy storage: it may happen that power is being purchased from the power grid at a favorable price for the end user and either using it to charge the home battery during a certain amount of time or using the battery stored energy to power home appliances is not the optimal solution for the end user. Thus, the algorithm will just keep the remaining energy of the battery stored. While there might be small changes in the charge and discharge that is applied to the battery in this lifespan, the overall tendency will be directed towards keeping a particular level of charge.
- Descending energy storage: here, energy is gradually discharged from the reservoir where it was kept. As in the two aforementioned strategies, it may happen that during the discharge process some charging is done, but it will be less significant than the tendency that is taking place during the discharge period of time. This strategy is useful when energy has been purchased at a comparatively low price and a high price cycle is taking place. On the contrary, energy discharges might take place when power is needed by the end users but buying it from the power grid is more expensive than desired (i.e., during peak hours). Typically, when that cycle ends or all the energy that used to be stored is depleted, this strategy will be put to a halt.
3.2. Business Model Canvas for End User and Third Party Symbiosis
- Key partners: The external entities required for the correct performance of the business are located in this part of the business canvas model. Being the proposals related to the power grid and Information and Communication Technologies, it should come as no surprise that they are a cloud infrastructure provider for all the use cases where a connection to a remote location is required (all but the first proposal), the Transmission System Operator (TSO) used to transfer the electricity traded from one location of the power grid to another and the battery manufacturer, which will provide the infrastructure for energy storage.
- Cost structure: This part of the canvas involves the activities that will suppose a cost to the company to be developed, even though they will be mandatory for the maintenance of the infrastructure used by it. Some of them are to be expected from any company (marketing promotion and sales to keep the flow of profits, along with customer service to guarantee that any punctual issue is solved in an effective manner for end users), whereas others will be focused on the assets used by the third party with the idea to keep them functional (battery replacement, Home Load Controller maintenance).
- Key activities: Here, the most critical activities to be done by the company have been placed. Without them, there is no possibility for the third party to provide any kind of service. There are four of them in this case: software development (focused on hardware heterogeneity abstraction and the Trading Algorithm), hardware connectivity (dealing with the required cabling and electric protocols to connect the appliances used under this model), hardware installation (putting the devices to be used in a location good enough to guarantee that there will be no damage for them, apart from the one derived from normal usage) and data collection (used when there are several users with different profiles, under an aggregator-enabled business model, that will be included in a cluster with the idea of using profile data to complement each of the end users).
- Key resources: The assets critical for the third party have been placed in this part of the business model canvas. There are four of them: home battery (for energy storage), Home Load Controller (to control and monitor how energy is being used in the facility where the battery is installed), the Trading Algorithm (used for energy balancing and, overall, to trade with the electricity that is stored in the facilities) and the cloud infrastructure (mandatory to store the algorithm in any business model that will depend on accessing the Trading Algorithm in a non-local scenario).
- Value proposition: The services that are offered to the clients of the third party have been placed in this section of the canvas. There are three of them to be offered: profits obtained from trading the electricity that has been gathered from the power grid in case it is sold at a higher price than the one it was purchased at, prevention of power cuts that may affect or damage any equipment used in the facilities where the batteries are installed and electricity cost reduction, since trade will make possible that the obtained benefits will make up for the electricity bill required to be paid to the energy supplier.
- Revenue streams: the ways that are going to be used to obtain a profit for the third party company have been placed in this part of the business canvas. They have been summarized as three different kinds, which correspond to the four business proposals that are put forward in the manuscript: the offline solution, the online solution that will require to have a contract signed, the online solution that will rely on sporadic connections to the aggregator platform to perform trading operations, and the fourth solution where electricity is provided to a cluster of users either from the one bought from the DSO or from other user with a different profile in the same cluster (hence, having the third party behaving as a DSO from the end user point of view).
- Relationships: The different instruments for the third party to relate to its customers are shown in this part of the business model canvas. The operations that are going to be performed by the third party are mostly defined on the grounds that any trade should be done in an automated manner that will require as little intervention from the end user as possible, so that the utilities installed will not interfere with their everyday life. In a way, this concept has some resemblance to the ones used for the Internet of Things, where according to Mark Weiser, computers should recede to the background and become integrated to the environment for their unconscious usage [28]. Therefore, automated management is enabled as a way to guarantee that services will be provided in that way. In addition to that, the permanence of the clients that receive the services provided by the third party company will be tailored according to the kind of business model that is applied in each of the specific cases. Last but not least, social networks will be used to get in touch with the clients as a way to obtain fast and cheap feedback from them, in case there are issues to be fixed.
- Channels: How the third party will become known for the customers of interest is what has been placed in this part of the business model. There are basically three channels that can be considered for the company: Smart Grid websites that will make popular the work that is being carried out by the company, the website used by the very third party involved in energy trading and the social network profiles of its customers, which will have the criteria to spread either positive or negative feedback from their experiences.
- Customer segments: The profiles of the customers that will be making use of the services provided by the third party company will be portrayed in this part of the business canvas model. Basically, every end user with a facility or location where energy is consumed will be a potential customer for the third party involved in these business models. The most prominent groups have been highlighted: (a) home dwellers that live in a house or a building; (b) public institutions that have public buildings that need electricity to work and therefore can use it to trade as any private home dweller; (c) Small and Medium Enterprise owners that have some facilities where electricity is required and its usage can be optimized; and (d) overall facility owners that will make use of energy and can therefore benefit from its storage and commercialization as a commodity by means of a battery, an Internet connection and a Trading Algorithm.
- Battery energy: At the time writing this manuscript, Tesla Powerwall is the most popular home battery used to store energy, either by itself or combined with Solar Photovoltaic (PV) installations. Consequently, its declared figures for energy (6.4 kWh), guaranteed cycles and cost have been used.
- Battery cycles: According to the official figures that have been presented by Tesla, it is estimated that the 6.4 kWh battery should last for approximately 5000 charge cycles without significant worsening of its performance, which is roughly 10 years of life expectancy, regarded as the period of time falling within the warranty offered with the battery [29,30].
- Battery cost: The cost of the battery that is used for the example is recommended to be $3,000. In addition to that, it is estimated that the cost to have it installed is around $500 [31] so the overall cost used for the examples will be $3,500.
- Electricity off-peak tariff: In this case, the data that were provided in [13] will be used for the example as off-peak and on-peak tariffs. Therefore, off-peak cost of the energy will be 0.04 €/kWh.
- Electricity on-peak tariff: Following the data provided before, on-peak tariff has been considered to be 0.12 €/kWh.
- Annual electricity consumption: Since the information regarding tariffs was obtained for the particular situation of Spain, an average value of the annual energy consumption in a Spanish home has been used [32]. This value is 3.487 MWh.
- Legislation: It has been considered that legislation should be kept out of these business models for two reasons: (a) it greatly varies from one country to another (even in the same country there might be different regulations and conditions); and (b) it may distort the results obtained in a negative (legislative constrains) or positive (fiscal incentives) manner. The main interest of this manuscript is describing the impact of energy storage and the Smart Grid (if available) for end users without external further interventions.
3.3. Business Model 1: Offline Model
3.3.1. Offline Model without the Smart Grid
- The energy storage solution is connected to the power grid as any other home appliance. Additionally, the Home Load Controller is connected to the same infrastructure and both monitors and controls energy usage in the power plugs where it has been given a degree of control, offering information at the will of the end user.
- The Trading Algorithm installed in the Home Load Controller will be responsible for the periods of time used to charge and discharge the energy reservoir. Commonly, it will favor charging the battery when electricity is cheaper (therefore, the Trading Algorithm will buy electricity) according to the pricing forecast that has been made previously available by the DSO. On the other hand, it will encourage offering energy to the power grid (thus selling electricity) when it is economically more convenient. This buying price, considered for Kilowatts-hour, has been named as Bpk.
- During the timespan when electricity is most expensive, the Trading Algorithm will receive as input the energy demanded by the home appliances of the facility where it has been installed, which is monitored by the Home Load Controller. Therefore, rather than having the dwell accessing the regular power grid to gather electricity (and thus, spending money on the procedure), energy will be obtained from what has been stored by the home battery and will save the end user the expenditures that would be incurred if the commodity had to be accessed from the market during the timespan when it is more costly.
- Furthermore, it is likely that not all the stored electricity will be required during a certain period of time. Consequently, it will be sold at a higher price than the one it was bought, as seen in Equation (1), where selling price per Kilowatt-hour (Spk) is higher than zero when Bpk is subtracted from it, obtaining a User Profit per Kilowatt-hour (Upk). As a way to simplify the operations, it has been assumed that the energy that was acquired during the off-peak hours will be sold during the peak hours.Upk = Spk − Bpk
- Upk is assumed to be the difference between off-peak price (0.04 €/kWh) and on-peak price (0.12 €/kWh), since the former is the price where the algorithm is triggered to buy energy and the latter the one used to sell it. Thus, the value is 0.12 − 0.04 = 0.08 €/kWh.
- Thus, by means of Equation (2) and the fact that Tesla Powerwall can store up to 6.4 kWh of energy, it can be said that for every charge cycle, the end user will have generated 0.08 €/kWh × 6.4 kWh/cycle = 0.512 €/cycle from the stored energy, as it is either poured into the grid or used in the dwelling or facility where the battery is located (and therefore power does not have to be purchased).
- The break-even point BEP will be reached when the $3,500 made as the investment have paid off by means of the savings or profits done when trading with the energy stored. At the time of writing this manuscript, 1 € has an exchange rate of 1.11811 USD, resulting in $3,500 being exchanged at 3,130.14 €. Considering the result obtained previously, it can be stated that the number of cycles Nc required to get to the BEP is 3,130.14 €/0.512 (€/cycle) = 6113.56 charge cycles, or 6114 as an integer number. This result is past what Tesla estimates as the typical lifetime of a Powerwall battery (around 5000 charges).
- Additionally, considering 3.487 MWh as the yearly electrical consumption of a Spanish household, it can be said that 3.487 MWh/6.4 (kWh/cycle) = 544.84 charge cycles are performed yearly. This figure is higher than the one effectively provided (5000 charge cycles/10 years = 500 cycles/year), so even if possible, using the battery alone to provide energy would result in an overuse that would rapidly degrade it. Besides, if the former number of charge cycles to pay the battery off is considered, it will have to be used for 6114 cycles/545 (cycles/year) = 11.22 years, which goes beyond the warranty established for the product. At this point, underperformance of the acquired battery is very likely to happen and it will have to be extended even during a longer period of time.
- The estimations done here do not take into account other factors, such as temperature, that are likely to worsen the performance offered by the home battery.
3.3.2. Offline Model within the Smart Grid
- Upk is still the same as the one that was calculated before with the available data, that is, 0.12 − 0.04 = 0.08 €/kWh.
- Although the User Profit per battery charge cycle is still the same as the one obtained before, the profit obtained from the Smart Grid installation is added to the general result. Consequently, gross profits are not only 0.512 €/cycle but also 0.08 €/h that are obtained from the Smart Grid installation. Ideally, the microgrid deployment will be able to provide enough electricity during the peak hours that, according to the data obtained in [10], can be roughly estimated as 10 h/day. Thus, daily added profit inferred from Equation (7) is 0.8 €. Considering that 545 charge cycles are performed every year, 545 (cycles/year)/365 (days/year) = 1.49 charges are made as an average value every day, so a profit of 1.49 × 0.512 = 0.76 € are obtained every day from the home battery. Overall = Upcsg = 0.8 (€/day) + 0.76 (€/day) = 1.56 €/day.
- Taking the latter figure into account, it can be said that the final time require to have the home battery paid off is 3130.14 €/(1.56 €/day × 365 (days/year)) = 5.5 years. This is a much more reasonable scenario than the one where no Smart Grid is used at all, since the timespan required is well within the warranty offered by the Tesla Powerwall used in the example.
3.4. Business Model 2: Online Fixed Time
3.4.1. Online Fixed Time without the Smart Grid
- The pieces of equipment are plugged to the power grid and connected with each other in the same fashion that was used in the previous case, as if they were regular home appliances.
- While the Trading Algorithm is still responsible for the trading activities carried out by the overall proposal, it works in a different way, as it will be remotely accessed every time that an operation of either purchasing or selling electricity is done. Consequently, trading operations will be done by means of the support provided by the platform during the specified period of time that was arranged in the contract.
- When trade is done and a profit is obtained (as it was done before, mostly by buying energy when it is cheaper and selling it when it is more expensive) a percentage will be deduced from the profits made by the user to make up for the reduced cost of the hardware infrastructure offered in this use case scenario and the cloud platform that is used to store the algorithm.
- The operations used to define when electricity will be spent by the end user, sold or stored will work as they were described before. If the contract is cancelled, then the access to the platform is shutdown, but the end user will still keep the hardware that has been purchased, provided that they hired the services of the third party company for a minimum amount of time.
- Upk is still the same, so it is estimated to be 0.08 €/kWh according to the estimations previously done.
- According to the previous calculations done, the home battery still makes possible earnings of 0.512 €/cycle.
- In this case, it is considered that the home battery is offered by the third party company, so there is not a concept of “breaking even” for the end user, since the only investment that has to be done from their side is signing a contract where they will commit to allow the company to provide the end user with electricity. If the 3130.14 € budget for home battery purchase and installation is maintained, then there are two sources of profits that must be taken into account by the end user and the third party company in order to have a fair share of both of them.
- Depending on the contracted terms, Platform charges may be lower or higher depending on the timespan defined, as well as the share that the third party company might take from the earnings per cycle. Should the third party not take anything from the latter, either the platform charges will be higher, or a longer timespan will be negotiated with the end user. This business model offers a high degree of flexibility because there are several revenue sources that can be used to both partners’ advantage.
3.4.2. Online Fixed Time within the Smart Grid
- Upk has not varied from the previous scenario, being 0.08 €/kWh.
- The earnings obtained on a daily basis from the Smart Grid installation and the home battery are still the same, that is, 1.56 €/day.
- The time required to pay off the battery is not relevant, as it is provided by the third party company. However, more flexibility can be offered to the end user here, as they already have an installation (PV, wind turbine, etc.) that, while not related to the facilities provided by the third party company, provides the prosumers with benefits.
- Consequently, having the home battery paying off by itself is a matter to be solved for the third party company. It will be done so by at least using Equation (12), since the revenues provided from the battery cannot be the only source used for profitability.
- For example, if the only objective of the company was paying off the investment it does on the home battery and its installation it can be said that 3130.14 € = Pch·t. For a period of 10 years, annual charges to the platform must be 313.01 €, or a monthly tariff of 26.08 €. On the other hand, if a third of the daily profits made with the home battery where to be included, then the third party company would have gained 1898 € by the end of the 10-year period from the battery itself, so it would have just to make 3130.14 − 1898 = 1232.14 € from the platform access charging to have the home battery fully repaid, which would be 10.27 € every month during 10 years. This estimation, as well as the other ones, is likely to improve in the future, as battery prices have a decreasing tendency, so shorter periods of time can be enabled if required.
3.5. Business Model 3: Online Access-Demand
3.5.1. Online Access-Demand without the Smart Grid
- The appliances used to store energy (home battery) and monitor the patterns used to consume it (Home Load Controller) are connected to the power grid as it was done before. Their usage of electricity is the same as in the other cases.
- Profitability for the end user and the company is guaranteed in a quite similar way too: actions performed by the Trade Algorithm used in order to buy, store, or sell electricity will guarantee an economical benefit in those operations. The Trading Algorithm is still located in the third party platform and used in a remote manner.
- However, the equipment installed in the client´s facilities (that is to say, the Home Load Controller) may access the platform at will to make use of the algorithm, in a way that will be defined according to the agreement signed in the contract that links both the end user/client and the third party company. While the same formulae shown in the previous business model proposal can be applied here, they will differ in the Business profit based on the Platform charge or Pch, as it will be lower to reflect the fact that the platform is accessed during a less significant amount of time and therefore is available for longer periods of time for other users.
- Charges done on the hardware equipment that is purchased by the end user (if required) will be different in this business model as well: they will be higher to make up for the lesser access to the platform made by the end user, although they will not be at the level of the offline business model, as the Trading Algorithm is still located out of the hardware that is brought to the dwell of the end user/client.
3.5.2. Online Access-Demand within the Smart Grid
3.6. Business Model 4: Enhanced Cooperation
- Energy stored by the different end users of a cluster. Trading operations will be done not only taking into account the cost of electricity in the market, but also the energy availability of each of the end users, in case they have energy available that can be transferred from a user that has no energy and needs it, from another one that has unused energy.
- User profiles stored by the third party company will be accessed to look for the end users or users that are potentially the best match in terms of electricity usage (for example, if a user stores energy from 7:00 a.m. to 7:00 p.m., and there is another one consuming energy from 7:00 a.m. to 7:00 p.m., they will be born in mind to provide energy supply to each other).
- The appliances that were installed locally in previous businesses models will be installed here too. However, the information regarding different users in a certain area will be gathered as a cluster at a higher level that will be oblivious to them, but not to the power grid. This level is where data aggregation will be made by the third party company, and will provide the necessary user profiles that will be taken into account for energy trade among several members of the cluster where all of them have been integrated.
- Electricity is bought, stored and sold in the same way that it was done before, as there is still a Trading Algorithm that is stored remotely in the domains of the third party company. There is a major difference in this step, though, which deals with how the operations are done by the algorithm, due to the fact that it will take into account not only the cost of electricity in the market, but also the electricity offer and demand situation in the cluster of users that has been built before.
- Under this model, Platform charge (Pch) will be the lowest one that the end user will be required to pay, since earnings will be obtained from allowing the third party company to access the energy storage equipment installed in the dwells of the end users.
- Most of the earnings obtained by the third party company will come from their operation as if they were a DSO providing electricity for end users, albeit the way utilized to obtain the commodity greatly differs from the one use by a conventional DSO. Hence, the third party company will be regarded as a “virtual DSO” for the end users. As it has been represented in Figure 5, the third party company will play the role of an aggregator, so it will trade with the energy provided by the end users in the first place. Ideally, the net balance of the energy that is to be traded among the users will be zero, as the most desirable situation is that a whole cluster of users will not need any other power injection from any other entity, but if it is required, energy offered by the DSOs will be acquired in a punctual manner.
4. Study of Related Use Cases
4.1. Simulated Scenarios
- In the use case involving a large San Francisco hotel, if a threshold of 500 kW is fixed as the power consumed every day, should a perfect load forecast knowledge be assumed, a system sized 140 kW and 560 kWh would be able to supply the hotel with the energy needed not to surpass the threshold that was chosen before. This will be done so by charging the energy reservoir mostly during off-peak daily hours (8:00 and 9:00 a.m. and 4:00–7:00 p.m.) and discharging it during on-peak hours (10:00 a.m., 8:00–11:00 p.m.). By following this procedure, a demand charge reduction can be guaranteed and, over a period of 20 years, revenues of around $100,000 will be obtained, even when the cost of substituting the batteries is also taken into account.
- As for the distribution upgrade deferral of New York, it is based on a proposal that was made so as to avoid the taxpayers the installation of two substation upgrades in Brooklyn and Queens that were expected to cost $1,000 million. According to the authors of the report, demand response and energy efficiency programs could be used as a tool that would reduce load growth by a 50%. Furthermore, an energy storage fleet would also be provided to reinforce the aforementioned policies. In this particular case, the cost of the solution was reckoned to be higher than the revenues that it would provide in 20 years (more than $250 million of costs compared to $200 million revenues) due to the fact that batteries do not provide direct services to the end users. However, no value has been given to backup power under this model, so a less strict model is likely to produce more favorable results.
- Residential bill management in Arizona takes advantage of rooftop photovoltaic systems combined with energy storage. The model matches the typical pattern where solar power production reaches its maximum levels around noon, is lower before and after them, and is nonexistent at night. It is shown how energy consumption flattens whenever there is energy storage enabled, as it effectively shaves off demand peaks and makes possible a more constant energy usage during the night. Under this model, it is estimated that customer bills can be reduced up to a 20% every year. One of the conclusions of this use case is that prosumers with “peaky” consumption patterns (namely, those who consume electricity in a more irregular manner with load peaks rather than in a more regular way) are the ones that can benefit the most from energy storage solutions.
- Solar self-consumption in San Francisco uses the same ideas that were presented in the residential bill management case: due to customer loads and solar radiation, batteries will be charged in the period between 8:00 and 11:00 a.m. and discharged from 6:00 to 11:00 p.m. The policy followed according to this model is that the excess power produced by the PV installation will firstly be used to charge the battery and once this process is completed the generated electricity will be exported to the grid. As was described in the previous business models, by following this pattern, it will be possible to use the stored energy to supply electricity to the loads used during the evening (peak hours), thus preventing the customer from purchasing electricity from the grid.
4.2. Large Energy Storage Facilities
4.3. NETfficient Research Project
4.4. Steinkjer Living Lab Pilot
Steinkjer Use Case Description
- Average value: The average time to attend a query done through the middleware was 1602 ms, a satisfactory result for end clients. Interestingly, this value does not increase significantly when more requests are sent to the system, showing a remarkable degree of robustness.
- Median value: Rather than its own value (1582 ms, still satisfactory), the small difference between average and median values is significant. This proves that queries were dealt with in a rather stable manner, without significant delays or punctual underperformance moments that would distort the average value away from the median one.
- Deviation value: It reflects the stability hinted by the narrow difference between the average and median values. The dispersion level in the tests done shows that figures obtained for each of the queries vary in a small proportion, as deviation represents less than one quarter of the average and median values (364 ms).
- Throughput value: this value shows the number of concurrent client requests that could be answered in a specific amount of time, resulting in nearly 150 queries answered every minute (148.543 queries/min), a result that can again be regarded as satisfactory for end clients.
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Name | Dimensions | Weight | Operating Temperatures | Voltage | Current | Power | Energy |
---|---|---|---|---|---|---|---|
Tesla Powerwall | 130 cm × 86 cm × 18 cm | 97 Kg | −20 °C to 43 °C | 350–450 VDC | 9.5 A | 3.3 kW | 6.4 kWh |
Panasonic LJ-SK84A | 138 cm × 96.6 cm × 27.9 cm | 84 Kg (159 Kg with batteries) | 0 °C to 40 °C | 230 ADC | 8.7 A | 2.0 kW | 8.0 kWh |
Nissan’s xStorage | -- | -- | -- | -- | -- | -- | 4.2 kWh |
Orison Tower/Orison Panel | 86.36 cm × 22.86 cm (Tower)/55.88 × 71.12 cm (Panel) | 18.14 Kg (Tower)/17.24 Kg (Panel) | -- | 120 ADC | 15 A | 1.8 kW continuous, 3.5 kW peak | 2.2 kWh |
Mercedes-Benz Power Pack | -- | 29.94 Kg | -- | -- | -- | -- | 2.5 kWh per pack (up to 20 kWh) |
BYD Mini | 68 × 25.6 × 61 cm | 96 Kg | 0 °C to 40 °C | 240 V | -- | 3 kW | 3 kWh |
Powervault (Lithium ion/lead acid) | 82 × 58 × 50 cm | 85 to 125 Kg (Li)/185 to 245 Kg | 0 °C to 35 °C | 217 to 253 V | 75 A | 1.6 kW (peak power) | 2.2 kWh to 6.6 kWh (Li)/6.6 kWh to 8.8 kWh |
sonnenBatterie eco | 129.54 or 180.34 × 66 × 35.56 cm | -- | 5 °C to 35 °C | 240 V | -- | 3 to 8 kW | 4–8 kWh, 10–16 kWh |
Business Model Proposition | Customer Profitability | Third Party Profitability | Action Description |
---|---|---|---|
Offline hardware sell | Breaking even point | Hardware (battery + Home Load Controller) with embedded software | The consumer purchases a battery with a Home Load Controller and a locally installed Trading Algorithm |
Online fixed time | User profit from trade | Hardware sell, business profit from platform access during a certain amount of time | The consumer purchases a battery with a Home Load Controller and access a remote platform with the Trading Algorithm during a certain amount of time |
Online variable time | User profit from trade | Hardware sell, business profit from accessing the platform during particular instants | The consumer purchases a battery with a Home Load Controller and access a remote platform with the Trading Algorithm during a particular moments |
Online distributed trade | User profit from trade | Hardware sell, “virtual DSO” operations | End users are interconnected among them to interchange electricity without using any other entity but the third party |
Input | Motivation for “Buy” Operation | Motivation for “Sell” Operation |
---|---|---|
Energy consumption forecast | Current electricity price is low, but it will be high in the foreseeable future | Current electricity price is high, but it will be low in the foreseeable future |
Weather forecast | Future weather conditions will make Distributed Energy Resources or Renewable Energy Sources (if available) scarce in the foreseeable future | Future weather conditions will make Distributed Energy Resources or Renewable Energy Sources (if available) abundant in the foreseeable future |
User Profiles | Energy is used during the current timespan by home dwellers | Energy is either not used or stored during the current timespan by home dwellers |
Agents Present in the Power Grid | Agents that will Benefit from End User Business Models | Agents that Must Readapt to End User Business Models | Reasoning for the Shift in Their Procedures |
---|---|---|---|
Energy reservoir manufacturers | Yes | No | A way to store the asset to sell it at the most suitable moment is required. Cost is expected to fall as time goes by |
Advanced Metering Infrastructure manufacturers | Yes | No | Electricity consumption must be measured accurately to better trade with it |
Aggregator | Yes | No | Its appearance will make possible the usage of electricity in a way that users will balance with each other to an extent. Potential demand for a DSO will be reduced |
Distributed System Operators | No | Yes | Demand of electricity will be partially covered by the Aggregator and Distributed Energy Resources |
Transmission System Operators | No | Yes | Electricity will be transferred in two ways rather than one. End user level produced electricity will greatly vary from one moment to another |
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Rodríguez-Molina, J.; Martínez, J.-F.; Castillejo, P. A Study on Applicability of Distributed Energy Generation, Storage and Consumption within Small Scale Facilities. Energies 2016, 9, 745. https://doi.org/10.3390/en9090745
Rodríguez-Molina J, Martínez J-F, Castillejo P. A Study on Applicability of Distributed Energy Generation, Storage and Consumption within Small Scale Facilities. Energies. 2016; 9(9):745. https://doi.org/10.3390/en9090745
Chicago/Turabian StyleRodríguez-Molina, Jesús, José-Fernán Martínez, and Pedro Castillejo. 2016. "A Study on Applicability of Distributed Energy Generation, Storage and Consumption within Small Scale Facilities" Energies 9, no. 9: 745. https://doi.org/10.3390/en9090745