WO2015018465A2

WO2015018465A2 - Method, system and controlling means for load balancing between local nodes

Info

Publication number: WO2015018465A2
Application number: PCT/EP2013/077237
Authority: WO
Inventors: Maja ETINSKI; Anett Schuelke
Original assignee: Nec Europe Ltd.
Priority date: 2013-08-05
Filing date: 2013-12-18
Publication date: 2015-02-12
Also published as: JP6281156B2; JP2016527864A; WO2015018465A3; EP3031114A2

Abstract

The present invention relates to a method for load balancing between entities in a local node community, wherein a plurality of nodes and at least one storing means for storing energy forms the local node community and wherein a local node comprises generation means for generating energy and load means for consuming energy, and wherein the local nodes and said storing means are connected with each other for exchanging energy, wherein the energy within the local node community for load balancing is assigned to be transferred from one or more local nodes and/or from the storing means offering energy to the local node community to one or more local nodes and/or to the storing means having an energy demand and that the said assigned energy is assigned to be at least partially transferred back later to the one or more entities having offered the energy. The present invention further relates to a system for load balancing between local nodes in a local node community and to controlling means for controlling load balancing.

Description

METHOD, SYSTEM AND CONTROLLING MEANS

FOR LOAD BALANCING BETWEEN LOCAL NODES

The present invention relates to a method for load balancing between entities in a local node community, wherein a plurality of nodes and at least one storing means for storing energy forms the local node community and wherein a local node comprises generation means for generating energy and load means for consuming energy, and wherein the local nodes and said storing means are connected with each other for exchanging energy.

The present invention further relates to a system for load balancing between entities in a local node community, wherein a plurality of nodes and at least one storing means for storing energy forms the local node community and wherein a local node comprises generation means for generating energy and load means for consuming energy, and wherein the local nodes and said storing means are connected with each other for exchanging energy, preferably for performing with a method according to one of the claims 1 -14.

The present invention even further relates to controlling means for load balancing between entities in a local node community, wherein a plurality of nodes and at least one storing means for storing energy forms the local node community and wherein a local node comprises generation means for generating energy and load means for consuming energy, and wherein the local nodes and said storing means are connected with each other for exchanging energy, preferably for performing with a method according to one of the claims 1 -14 and/or a system according to claim 15.

Although applicable to energy in general the present invention described with regard to electric energy.

Although applicable to generation means for generating energy in general, the present invention will be described with regard to renewable energy sources. Renewable energy systems like solar panels or the like are become more and more available and easier to deploy even in urban areas. Many governments tend to further increase the portion of renewable energy sources in the total energy consumption resulting in ubiquity of both utility scale and residential renewable energy source systems. Such residential renewable energy source systems have been encouraged for example through guaranteed feed-in rates into power grids but a decrease in costs of such systems leads to a reduction of the need of guaranteed feed-in rates since local energy consumption is getting more economical. Local energy consumption and generation may be used to get more independence from an external power grid or a devotion of the user to green energy in general.

A wide use of so-called distributed energy resources like a plurality of households each representing renewable energy sources and energy consumers have to be integrated into existing power grids without leading to stability problems due to the impact of locally stochastically fed-in energy.

In the non-patent-literature of M. He, E. Reutzel, X. Jiang, R. Katz, S. Sanders, D. Culler, K. Lutz, "An Architecture for Local Energy Generation, Distribution, and Sharing." IEEE Energy2030, Atlanta, Georgia, USA, November 2008, a network of local nodes is proposed. Each node comprises a power generation source like a solar panel or the like, storage means, for example a battery and loads, for example electric devices like a refrigerator, television, etc.. Each of these nodes is connected with each other via a local grid forming a local grid segment. This grid segment is then further connected to a power grid. Energy routing between nodes is here seen in an accounting sense, since only the net flow of energy is relevant for energy sharing among these nodes.

It is therefore an objective of the present invention to provide a method, a system and controlling means for load balancing between entities in a local node community. It is a further objective of the present invention to provide a method, a system and controlling means for load balancing between entities in a local node community, which enable a high utilization of local fluctuating energy sources. It is an even further objective of the present invention to provide a method, a system and controlling means for load balancing between entities in a local node community enabling in a flexible way to expand or decrease the number of local nodes within the local node community without the need to re-optimize or change the underlying load balancing principle.

It is an even further objective of the present invention to provide a method, a system and controlling means for load balancing between entities in a local node community, which are cost-effective, in particular with regard to buy or sell energy from or to a utility or an external grid.

The aforementioned objectives are accomplished by a method of claim 1.

In claim 1 a method for load balancing between entities in a local node community is defined, wherein a plurality of nodes and at least one storing means for storing energy forms the local node community and wherein a local node comprises generation means for generating energy and load means for consuming energy, and wherein the local nodes and said storing means are connected with each other for exchanging energy. According to claim 1 the method is characterized in that the energy within the local node community for load balancing is assigned to be transferred from one or more local nodes and/or from the storing means offering energy to the local node community to one or more local nodes and/or to the storing means having an energy demand and that the said assigned energy is assigned to be at least partially transferred back later to the one or more entities having offered the energy.

The aforementioned objectives are also accomplished by a system of claim 15. In claim 15 a system for load balancing between entities in a local node community is defined, wherein a plurality of nodes and at least one storing means for storing energy forms the local node community and wherein a local node comprises generation means for generating energy and load means for consuming energy, and wherein the local nodes and said storing means are connected with each other for exchanging energy, preferably for performing with a method according to one of the claims 1 -14.

According to claim 15 the system is characterized by controlling means operable to assign the energy within the local node community for load balancing to be transferred from one or more local nodes and/or from the storing means offering energy to the local node community to one or more local nodes and/or to the storing means having an energy demand and to assign an at least partially back transfer later to the one or more entities having offered the energy.

The aforementioned objectives are also accomplished by controlling means of claim 16.

In claim 16 controlling means for controlling load balancing between entities in a local node community is defined, wherein a plurality of nodes and at least one storing means for storing energy forms the local node community and wherein a local node comprises generation means for generating energy and load means for consuming energy, and wherein the local nodes and said storing means are connected with each other for exchanging energy, preferably for performing with a method according to one of the claims 1 -14 and/or a system according to claim 15.

According to claim 16, the controlling means is operable to assign the energy within the local node community for load balancing to be transferred from one or more local nodes and/or from the storing means offering energy to the local node community to one or more local nodes and/or to the storing means having an energy demand and to assign an at least partially back transfer later to the one or more entities having offered the energy. According to the invention it has been recognized that an energy sharing and lending within the local node community is provided enabling a collaborative load balancing accompanied with energy efficiency in the local node community. According to the invention it has been further recognized that the locally generated energy by the local nodes can be distributed among the other entities resulting in a higher local energy utilization without the need to sell it to or to buy from a utility.

According to the invention it has been further recognized that by sharing the energy to the community of local nodes the direct involvement of the community members in the way of interacting towards the network is provided. It raises the community members' awareness and willingness into sharing the energy generated by themselves realizing direct sharing "face". According to the invention it has been further recognized that a realization of a local market place is enabled allowing a trading between small producers and users of energy within the local node community via the connecting grid as a physical infrastructure for this purpose. According to the invention it has been further recognized, that an efficient energy routing is provided balancing needs of local generation and demand.

According to the invention is has been even further recognized that by transferring back the energy later costs for buying energy from an external utility for compensation are significantly reduced.

According to the invention is has been further recognized that flexibility is enhanced since addition or deletion of nodes of the local node community can be performed without changing the underlying collaborative load balancing principle.

In other words the present invention provides a virtual storage system with the local node community based on inter-neighbor sharing relationships for the realization of collaborative load balancing within the local node community formed for example in a grid segment in the distribution grid. The energy surplus available in one local node can be in particular used to balance another local node or a storage entity within the local node community currently demanding more energy or power than it generates or to increase the state-of-charge of the storage entity. A direct use of locally generated energy is enabled.

Further features, advantages and preferred embodiments are described in the following subclaims.

According to a preferred embodiment the local node community is connected to a utility, preferably a power grid, so that when after assigning all energy offered within the local node community a total energy surplus or a total energy demand of the local node community is determined, the respective difference in energy is assigned to be exchanged with the utility to minimize the total energy surplus or energy demand of the local node community. This enables to first perform load balancing within the local node community and after that exchanging energy with a utility. Therefore costs for consuming energy are significantly reduced since energy from the utility is only bought when necessary.

According to a further preferred embodiment for determining the energy to be offered or to be demanded entity surplus information is provided by the corresponding entity based on the received energy from the local node community and/or the utility and the provided energy to the local node community and/or the utility. This enables in an easy way to provide entity surplus information since the corresponding entity provides the information itself. Further in the entity surplus information received energy and/or provided energy to the local node community and/or to the utility is included so that an efficient sharing and lending of energy is enabled.

According to a further preferred embodiment entity surplus information is provided in form of positive and negative values indicating a surplus in demand or generation. This enables in an easy way to provide the entity surplus information represented by a single value, wherein a positive value indicates a generation surplus and a negative indicates a load surplus respectively demand surplus. According to a further preferred embodiment actual and historic entity energy surplus and/or demand data is included in the entity surplus information. This provides a more balanced energy distribution within the local node community, since not only actual but also historic entity energy surplus data may be used for controlling the actual energy flow between the local entities of the local node community.

According to a further preferred embodiment assigning of the energy is based on one or more global energy sharing preferences and/or entity energy sharing preferences. This enhances the flexibility for controlling the energy flow, since e.g. individual preferences of users of a local node may be incorporated in the collaboration process for the load balancing, i.e. which energy from which local node or storage means is assigned to be transferred to another local node or storage means. Sharing preferences may also adapted dynamically, e.g. when the net flow of energy to one local node is only negative for a certain amount of time, the provision of the energy to this local node may be forbidden.

According to a further preferred embodiment assigning of the energy is based on one or more priority policies, preferably which are based on an entity energy sharing history. Priority policies may be incorporated into sharing preferences. For instance a priority policy may define that prior to sharing energy with other nodes a storage of the local node itself is filled up to a certain extent, so that an internal load balancing within the local node itself by providing energy from its own storage is preferred. In general priority policies or prioritization schemes can be used for energy assignment to a local node currently requesting energy. Thus further the flexibility for assigning the energy flow between the local nodes and/or storage means is enhanced.

According to a further preferred embodiment storing means are included into one or more local nodes of the local node community, preferably in each local node. By including storing means an even more efficient balancing can be provided, since for example a surplus of generated energy within the local node can be stored and used later internally prior to requesting energy from the local node community when a load surplus of the local node is determined. According to a further preferred embodiment a charging and/or discharging rate and/or the state of charge of storing means are included in the entity surplus information. This further enables an effective assigning of the energy between nodes for load balancing since charging or discharging rates as well as the state of charge of storing means are taken into account when transferring energies from one local node to another local node within the local node community. For example a high charging rate enables to store energy very fast, however a low discharging rate of one storage means may require activating other storage means to provide sufficient energy according to the actual demand, since the discharging rate of the one storage means may be too low.

According to a further preferred embodiment the entity surplus information is periodically updated and the assigning of the energy is updated accordingly. This enables for example an energy routing scheme for a next time interval defining how much energy lending local nodes must feed-in into the local node community. When the frequency of the periodic update is high, then it can be assumed that there is no change of entity surplus information, i.e. of local generation and local demand within this (short) time interval. Of course periodic updates can be further adapted, preferably dynamically for example according to the daytime. During night it may be sufficient to reduce the frequency of updating, since generation of energy due to solar panels is offline and changing of environmental conditions with regard to solar power is reduced. According to a further preferred embodiment for each pair of entities in the local node community a credit value is assigned to each entity indicating its actual and/or historic energy imbalance to the other entity of the pair. This enables to efficiently establish an inter-neighbor relationship among the local nodes based on their sharing history. Thus, inter-neighbor relationships are established enabling for example an easy analysis of the energy flow between the entities in the local node community.

According to a further preferred embodiment for assigning the energy, the entities, preferably the local nodes in the local node community, are sorted according to their current energy imbalance in decreasing order. This enables an easy determination how much energy lending nodes must feed-in into the local node community and how much energy borrowing nodes can get from the local node community. Further a fast and easy determination of the entities with power imbalance is enabled.

According to a further preferred embodiment when after load balancing between local entities within the local node community a positive total surplus in energy of the local node community is determined, the energy in excess is stored in one or more local storage means within the local node community. This reduces further energy to be bought from a utility since energy in excess is stored within the local node community. This energy can then be distributed among the entities, e.g. the local nodes with load surplus state in a future time interval to avoid buying energy from the external utility.

According to a further preferred embodiment energy excess of one or more local nodes before or after load balancing is stored in each local node having a respective storage capacity. This enables an even more effective balancing since for example each local node can load balance itself to avoid to request or distribute energy from or among the other local nodes within the local node community.

There are several ways how to design and further develop the teaching of the present invention in an advantageous way. To this end it is to be referred to the patent claims subordinate to patent claim 1 on the one hand and to the following explanation of preferred embodiments of the invention by way of example, illustrated by the figure on the other hand. In connection with the explanation of the preferred embodiments of the invention by the aid of the figure, generally preferred embodiments and further developments of the teaching will be explained.

In the drawings shows Fig. 1 a local node community of a system according to a first embodiment of the present invention;

Fig. 2 part of a method according to a second embodiment of the present invention; part of a method according to a third embodiment of the present invention; the impact of the battery sizes in a system according to a fourth embodiment of the present invention; total generation and load of all nodes of a system according to a fifth embodiment of the present invention; inputs in form of load and generation per local node of a system according to a sixth embodiment of the present invention; local load served from grid energy comparing a system with conventional load balancing and a system according to a seventh embodiment of the present invention; local node community behavior over time for a conventional system and a system according to an eighth embodiment of the present invention;

Figs. 9a, b inter-node balancing and energy flows of a system according to a ninth embodiment of the present invention for giving nodes (Fig. 9a) and receiving nodes (Fig. 9b);

Fig. 10 total transferred power over time of a system according to a tenth embodiment of the present invention; a comparison with regard to the number of battery cycles of different nodes between a conventional system performing load balancing and a system according to an eleventh embodiment of the present invention; total generation and load of all nodes of a system according to a twelfth embodiment of the present invention; inputs per local node of a system according to a thirteenth embodiment of the present invention; local load served from grid energy for conventional load balancing and load balancing according to a method according to a fourteenth embodiment of the present invention; local node community behavior over time for a system with conventional load balancing and for a system according to an fifteenth embodiment of the present invention; inter-node balancing and energy flows according to a sixteenth embodiment of the present invention for giving nodes (Fig. 16a) and receiving nodes (Fig. 16b); total transferred power over time according to a seventeenth embodiment of the present invention; a comparison with regard to the number of battery cycles of different nodes between conventional load balancing and a system according to an eighteenth embodiment of the present invention; steps of a method according to a nineteenth embodiment of the present invention;

per node energy consumption for a system according to a 20^th embodiment of the present invention; Fig. 21 the total power load and generation of all nodes of a systenn according to a 21 ^st embodiment of the present invention;

Fig. 22 the local power load and generation per local node of a system according to a 22^nd embodiment of the present invention;

Figs. 23a-c the portion of local load served by bought energy from a utility of a system according to a 23^rd embodiment of the present invention for different scenarios;

Figs. 24a-c the total transferred power among local nodes of a system according to a 24^th embodiment of the present invention for different scenarios; Figs. 25a-c the energy consumed from storage by local nodes of a system according to a 25^th embodiment of the present invention for different scenarios;

Figs. 26a-c the number of battery cycles per local node of a system according to a 26^th embodiment of the present invention for different scenarios;

Figs. 27a-c the total energy lent to other local nodes and borrowed from other local nodes per local node of a system according to a 27^th embodiment of the present invention for different scenarios;

Figs. 28a-c the portion of load served by utility energy with different balancing schemes including balancing schemes of a method according to a 28^th embodiment of the present invention for different scenarios; Figs. 29a-c a comparison between a conventional load balancing method and methods according to a 29^th and 30^th embodiment of the present invention for different scenarios; Figs. 30a-c a comparison between a conventional load balancing method and methods according to a 31^st and 32^nd embodiment of the present invention for different scenarios; and Figs. 31a-c a comparison of the number of battery cycles for a conventional load balancing method and methods according to the 31^st and 32^nd embodiment of the present invention for different scenarios.

Fig. 1 shows a local node community of a system according to a first embodiment of the present invention.

In Fig. 1 a distributed grid segment comprising multiple nodes is shown. A plurality of nodes 2, 3, 4, 5, 6 are each connected with each other forming a local node community 1. For example the local node community 1 may be seen as a network of local nodes 2, 3, 4, 5, 6 where each node 2, 3, 4, 5, 6 represents a house from the neighborhood. Each house features a local generation and a local storage. Energy routing is then physically performed over the existing power network of the houses connected with each other. The local node community 1 is further connected to an external power grid 10. Each node 2, 3, 4, 5, 6 comprises power generation means 2a, 3a, 4a, 5a, 6a, storage means 2b, 3b, 4b, 5b, 6b and a load 2c, 3c, 4c, 5c, 6c. Further controlling means CM are connected with each of the nodes 2, 3, 4, 5, 6 for controlling each node 2, 3, 4, 5, 6 in terms of providing energy to the local storage 2b, 3b, 4b, 5b, 6b as well as to balance power/energy generation with the load of the respective node 2, 3, 4, 5, 6 in the local node community 1.

Further the controlling means CM collect local node surplus information of each node 2, 3, 4, 5, 6 to determine which local node 2, 3, 4, 5, 6 of the local node community 1 may offer energy to other local nodes 2, 3, 4, 5, 6 or needs energy from other local nodes 2, 3, 4, 5, 6 within the local node community 1. Local nodes 2, 3, 4, 5, 6 that currently have an energy surplus are assigned to lend energy to the other local nodes 2, 3, 4, 5, 6 whose current demand exceeds their generation. The local storage 2b, 3b, 4b, 5b, 6b of each node 2, 3, 4, 5, 6 is used to partially balance between their own power demand and their own power generation. For example a typical electricity storage for homes is given in form of battery technologies. The controlling means CM reduce the use of these batteries since a more frequent use of these batteries results in a shorter battery life time. The available number of charging cycles is usually dependent on the maximum depth of discharge and charging. Therefore load balancing first within the local node community 1 itself improves the battery life time or in general the life time of the storages 2b, 3b, 4b, 5b, 6b of the local nodes 2, 3, 4, 5, 6. The controlling means CM is also responsible for determining the need for buying energy from the external power grid 10, if the total surplus state of the local node community 1 indicates that further energy for load balancing within the local node community 1 is needed and cannot be provided from the nodes 2, 3, 4, 5, 6 within the local node community 1. Also one or more of the nodes 2, 3, 4, 5, 6 may also determine the need for buying energy from the external power grid 10 itself.

A further example for a local node community 1 may be an electric vehicle charging system for collaborative neighborhoods. Power demand is for example driven by the owners wishing to charge their cars. An advantage is for example when neighbors that need their car sooner can be charged from other local nodes generation under condition that this energy will be returned or reimbursed in the future.

Fig. 2 shows part of a method according to a second embodiment of the present invention.

In Fig. 2 periodic power negotiations between the nodes 2, 3, 4, 5, 6 are shown over time. Different time intervals 30a, 30b are shown in which local generation 20 and local demand 21 is compared and the resulting power 22 is determined which is available for lending it to other nodes 3, 4, 5, 6. For example in the first time interval 30a the node 1 denoted with reference sign 2 has a surplus in local generation 20 over local demand 21 , whereas node 2, denoted with reference sign 3 has a higher local demand then its local generation. Therefore energy is assigned to be routed (reference sign 25) from the first node 2 to the second node 3. The node n-2 denoted with reference sign 4 has also a surplus in local generation of local demand. Therefore the energy is assigned to be routed to the second node 3. The nodes n-1 and n, denoted with reference signs 5 and 6, have in the first time interval 30a also the surplus in local generation over local demand. However, since the shown node 3 is balanced from the nodes with reference sign

2 and 4 the surplus in energy generation may be stored within the corresponding local nodes 5, 6.

In the second time interval 30b the first node 2 has a surplus in local demand over local generation. Therefore energy is assigned to be routed from the second node

3 which has a surplus in local generation over local demand since in the past, i.e. in the first time interval 30a the second node 3 has received energy from the first node 2. Therefore the energy is "balanced" over these two time intervals 30a and 30b. The node n-2, denoted with reference sign 4 has in the second time interval 30b a surplus in local generation over local demand. The surplus is sufficient to balance node n-1 and node n, denoted with reference signs 5, 6: Part of the energy surplus of node n-2 is assigned to be routed to node n-1 and another part of the energy surplus of node n-2 is assigned to be routed to node n. Therefore energy routing is based on periodically recomputed respectively negotiated power rates of routed energy considering the current power generation of each node 2, 3, 4, 5, 6, current power demand of each node 2, 3, 4, 5, 6, the state of charge of energy storage of each node 2, 3, 4, 5, 6 and a historical overview of energy that each node 2, 3, 4, 5, 6 has borrowed and lent. This negotiation is then periodically performed as depicted in Fig. 2. The result of the negotiation is an energy routing scheme for the next time interval, defining how much energy lending nodes must feed-in into the local node community grid and how much borrowing nodes can get from the local node community grid without paying for it to a utility 10.

Before negotiating the energy routing 25 the energy imbalance of each node 2, 3,

4, 5, 6 is computed, preferably based on its sharing preferences, its demand, its generation, the stored energy and the storage re- and/or discharging rate. Then the nodes 2, 3, 4, 5, 6 are in particular sorted according to selected criteria, for example the ones with higher surplus first and processed one by one, balancing the energy offer and demand from other nodes 2, 3, 4, 5, 6. When a local node's surplus energy is allocated to other nodes 2, 3, 4, 5, 6 requesting nodes 2, 3, 4, 5, 6 are sorted by decreasing priority. The priority may be equal to the number of credit units given to the local node 2, 3, 4, 5, 6 currently being processed. These power distribution steps are shown in Fig. 3.

Fig. 3 shows part of a method according to a third embodiment of the present invention.

In Fig. 3 in a first step S1 local node surplus information is generated based on current generation, current load and the state of charge of each node 2, 3, 4, 5, 6.

In a second step S2 the current power imbalance of each node 2, 3, 4, 5, 6 is determined.

Together with credit data for each pair of nodes 2, 3, 4, 5, 6 in a third step S3 the nodes 2, 3, 4, 5, 6 are sorted by their imbalance. For example the node i first lends energy to nodes 2, 3, 4, 5, 6 that have positive credits with this node 2, 3, 4, 5, 6. The second node k first lends energy to nodes 2, 3, 4, 5, 6 that have positive credits with this node k, etc.. In Fig. 3 the node i lends energy respectively power is transferred to nodes 1 , 2 and 3, wherein the assigned power to be transferred is denoted with reference signs P-i , P2 and P3, and node k is assigned to lend energy respectively transfer power to node 4 and 5, denoted with reference signs P₄ and P₅.

In a fourth step S4 the credit data is updated for the next negotiation process in the next time interval. "Negotiation" throughout the description preferably means that a centralized entity CM determines the energy imbalance of each node 2, 3, 4, 5, 6 and decides which of the nodes 2, 3, 4, 5, 6 and to what extent - given over the next time interval - offer energy/power and which node 2, 3, 4, 5, 6 is assigned to receive energy/power form other nodes 2, 3, 4, 5, 6 and to what extent - given over the next time interval - or that a collaborative/cooperative negotiation method among participating local nodes 2, 3, 4, 5, 6 within the local node community 1 is performed.

In the following Figs. 4 to 31 results for a load balancing according to embodiments of the present invention for a setup of ten local nodes, compared with conventional load balancing and for different scenarios are shown.

A simulation was performed for a period of a month with 31 days with negotiations performed every minute. The load trace is based on energy measurements made at a single household in France. The load trace is based on data of minute- granularity which is used for the negotiation load balancing process. The first month of the load trace corresponds to a first node, the second month is used for the power profile of a second node, etc.. Regarding the local generation half of the nodes feature solar panels and the other half feature wind mills. Solar irradiance used in the simulations corresponds to the measurement from US National Renewable Energy Laboratory (HSU, July 2012). The local nodes featuring solar panels have different solar panel sizes whose power outputs correspond to 2-5 times of per m² irradiance power. Wind data comes from pacific northwest of the United States and corresponds to January 2013. All wind mills have the same output. In the simulation setups it is assumed that all nodes have batteries with same specifications. Two cases were considered, smaller capacity batteries with 2kWh and 3kW and larger batteries with 6kWh and 3kW.

The results compare the collaborative load balancing according to embodiments of the invention and the intra-node balancing where local nodes use only their local storage for balancing, the latter is denoted with the term "baseline". The results show in summary that the amount of energy to be bought from a utility, e.g an external power grid by the local node community and by individual local nodes is reduced achieving at the same time longer battery life time through a lower number of battery cycles.

Fig. 4 shows battery sizes in a system according to a fourth embodiment of the present invention. ln Fig. 4 a comparison between the energy consumed from the external power grid 10 is shown for conventional load balancing 40a, 41 a compared with a load balancing according to the present invention denoted with reference signs 40b, 41 b, for the larger and the smaller battery size. Reference sign 41 corresponds to the smaller battery size and reference sign 40 corresponds to the larger battery size. The battery size further corresponds to the charging capability of the storing means of a local node. It can be seen that the energy consumed from the grid 10 based on an embodiment of a method according to the present invention ("collaborative") is smaller than the energy consumed according to the conventional load balancing ("baseline") without collaboration.

Fig. 5 shows total generation and load of all nodes according to a fifth embodiment of the present invention. In Fig. 5 the total generation based on solar and wind energy and the respective load for a local node community is shown, wherein each node contains a larger one of the batteries. On the vertical axis a respective power in W for total load and total generation is shown whereas on the horizontal axis the time is shown in minutes.

Fig. 6 shows inputs in form of local load and generation per local node according to a sixth embodiment of the present invention.

In Fig. 6 for each node a corresponding load and generation profile for power over time is shown. The first five nodes 1 -5 shown on the left side of Fig. 6 generate their power based on solar energy whereas the nodes 6-10 shown on the right side of Fig. 6 generate their power based on wind mills.

Fig. 7 shows local load served from grid energy comparing a system with conventional load balancing and a system according to a seventh embodiment of the present invention.

In Fig. 7 the local load served from an external grid as a percentage value is shown for baseline (reference sign 42a) and collaboration according to the invention (reference sign 42b) for each of the ten nodes N1 -N10. As it can be seen, the local load served from grid energy is significantly reduced in relation to the total energy consumed when collaborative load balancing according to an embodiment of the present invention is used.

Fig. 8 shows local node community behavior over time for a conventional system and a system according to an eighth embodiment of the present invention.

Fig. 8 shows on the upper left side the power requested or required for the local node community from the grid in Watt over time for baseline and collaborative balancing. On the upper right side the feed-in power over time for baseline and collaborative load balancing is shown. On the lower left side the battery discharging and on the lower right side the battery charging in Watt is shown over time for baseline balancing and collaborative balancing for all nodes in total.

Figs. 9a, b show inter-node balancing and energy flows of a system according to a ninth embodiment of the present invention for giving nodes (Fig. 9a) and receiving nodes (Fig. 9b). In Fig. 9a a histogram is shown indicating the giving nodes according to their node ID over time. The symbols indicate the receiving node, for example a cross indicates that energy is assigned to be transferred from a giving node to node 1 or a circle indicates that the giving node transmits energy assigned to node 3 according to an embodiment of the present invention.

In Fig. 9b the corresponding matrix for receiving nodes over time is shown. A cross for example indicates that the corresponding node is assigned to receive energy from node 1 and a circle indicates that the receiving node is assigned to receive energy from node 3 for example.

Figs. 9a and 9b show therefore inter-node-balancing and a corresponding collaboration energy flow between each pair of nodes. Fig. 10 shows total transferred power over time of a system according to a tenth embodiment of the present invention.

In Fig. 10 the total transferred power in Watt within the local node community between the nodes over time is shown. There is no transferred power for the conventional system termed "baseline" since there is no inter-node load balancing.

Fig. 1 1 shows a comparison with regard to the number of battery cycles of different nodes between a conventional system performing load balancing and a system according to an eleventh embodiment of the present invention for different nodes.

Fig. 1 1 shows the number of charging cycles for the conventional "baseline" (43a) and "collaborative" load balancing according to an embodiment of the present invention (reference sign 43b) for each node N1 -N10. As it can be seen from Fig. 1 1 the number of charging and recharging cycles of the batteries within each local node is significantly reduced when collaborative load balancing according to an embodiment of the present invention is performed. In Fig. 12-18 the corresponding figures to figures 5-1 1 are therefore shown for the battery of smaller type, i.e. wherein each node contains a 2kWh, 3kW battery.

Therefore Fig. 12 shows total generation and load of all nodes of a system according to a twelfth embodiment of the present invention. Fig. 13 shows inputs in form of local load and generation per local node of a system according to a thirteenth embodiment of the present invention. Fig. 14 shows local load served from grid energy for conventional load balancing and balancing according to a method according to a fourteenth embodiment of the present invention. Fig. 15 shows local node community behavior over time for a system with conventional load balancing and for a system according to a fifteenth embodiment of the present invention. Fig. 16 shows inter-node balancing and energy flows according to a sixteenth embodiment of the present invention for giving nodes (Fig. 16a) and receiving nodes (Fig. 16b). Fig. 17 shows total transferred power over time according to a seventeenth embodiment of the present invention and Fig. 18 shows a comparison with regard to the number of battery cycles of different nodes between conventional load balancing and a system according to an eighteenth embodiment of the present invention. Fig. 19 shows steps of a method according to a nineteenth embodiment of the present invention.

In Fig. 19 steps for energy assignment at the beginning of a time interval are shown.

Before performing the steps shown in Fig. 19 the current imbalance of each node considering current generation, power load and stored energy as well as credits describing inter-node relations between each pair of nodes are determined and used for the energy assignment process. A positive imbalance value means that the node currently produces more energy than needed within the node. On the other hand a negative imbalance means that the node's current power generation and power available from the local energy storage are not sufficient to cover the current load of the node. When computing a node's imbalance, different storage charging/discharging preferences may be used. For instance nodes may first try to use all locally stored energy before requesting energy from other local nodes within the local node community.

In the beginning the nodes are sorted according to their current imbalance in decreasing order which is shown in line 3 of Fig. 19. The nodes are then processed starting from the one with the highest positive imbalance. When a surplus node n is processed its energy surplus In is first offered to the nodes that have lent more energy in the past to the node n, since the procedure credit list (n) is listing all other nodes ordered decreasingly by the number of credits that the node n has with each of them. This is shown in lines 6-14. The transferred power is determined so that the energy requests are fully fulfilled if possible, otherwise at the maximum possible degree which is shown in lines 7-10.

A node receiving only a fraction of its requested energy still has an opportunity to get the rest from other nodes. After an energy transfer is agreed, i.e. the energy to be transferred is assigned, the current imbalances of the nodes taking part in the transfer are updated, which is shown in line 12 as well as their credits, which is shown in line 13 and the energy is transferred, preferably at constant power. By using storage preferences after a negotiation and transferring process and if some local nodes still have a positive imbalance, the energy excess is stored in a local storage for future consumption. The amount of energy which can be stored is limited by the energy capacity and recharging rate of the local energy storage. Similarly when the stored energy is consumed, the discharging rate has to be respected.

In the Figs. 20-31 the following system is used: The system comprises ten local nodes with an average distribution line loss of 7% used for all energy transactions. The same roundtrip efficiency of 90% is used for the local storage of each local node. This efficiency corresponds to high-efficiency lithium-ion batteries as disclosed in D. Rastler, "Electricity energy storage technology options: A white paper primer on applications, costs and benefits", EPRI, Palo Alto, CA, December 2010. Local storage specifications are used in form of battery specifications with 6kWh energy capacity and 2kW charging/discharging rates which is disclosed under "www.nec.co.jp/press/en/1 107/images/1301 -01 -01.pdf, where an example for NEC household energy storage system with lithium iron batteries is described. The simulated period used for the simulation lasts four weeks.

A load trace is generated using an electric power consumption data set of an individual household, which based on K. Bache and M. Lichman, "UCI machine learning repository", 2013. This used data set gives the total power consumption of a household in France measured over a period of almost four years starting from December 2006. The measurements are given with a one-minute sampling rate. This resolution is also used for the negotiation frequency. A different month of the data set was used to generate a load profile for each of the ten local nodes. Four weeks of each month are used starting from January 2008 until November 2008 excluding August when power consumption was very low due to a month-long absence of tenants. Missing values have been interpolated. By choosing different months to represent different nodes variants in power demand among the local nodes was achieved while still having a representative power profile of each local node. The energy demand of each local node over the simulated period of four weeks is shown in Fig. 20: Node N1 corresponding to January has the highest energy consumption while node N7 corresponding to July has the lowest. Therefore Fig. 20 shows the total energy consumption per node for the nodes N1 - N10 over the entire simulation period. Further in Figs. 20-31 different renewable energy sources are considered. There are three scenarios: In the first scenario all local nodes feature solar panels with solar energy generation ("Solar Generation"). In the second scenario wind mills with wind energy generation only are featured ("Wind Generation") and in the last case - the third scenario - the first half of nodes N1 -N5 are solar powered and the other half of the local nodes N6-N10 are wind powered. Thus this scenario is called "Mixed Generation". Solar and wind sources have different generation patterns as solar energy is available only during day while wind energy can be produced also over night. On the other hand a certain amount of solar energy is generated even on cloudy days providing regular daily power output while wind can be very weak over long time periods. All solar panels are assumed to have the same size with rated power of 2.85kW. Minute-resolution solar irradiants data disclosed in NREL "www.nrel.gov/midc" are used.

The first four weeks of May 2013 were used to generate a solar generation profile for each local node. Local nodes with solar panels have similar power outputs determined by the current solar irradiance and Gaussian noise N(0, 10kW) was added to reflect slight differences in generation among different local nodes due to parameters such as panel efficiency, panel orientation and local irradiance. Wind data is used from Bonneville Power Administration corresponding to the same period as the solar data, which is for example shown in "www.transmis- sion.bpa.gov/business/operations/wind/default.aspx" was scaled down to fit a rated output of 4kW that can be obtained with vertical axis wind mills suitable for residential areas, which is disclosed in "http://www.urbangreenenergy.com/pro- ducts/sanya-skypump". Wind-power local nodes get similar power outputs with the same Gaussian noise used for solar panels.

Fig. 21 shows the total power load and generation of all nodes of a system according to a 21 ^st embodiment of the present invention.

In Fig. 21 the total power load and generation of all ten local nodes over the simulated period of four weeks in kW is shown: On top of Fig. 21 the scenario for solar panels is shown, in the middle the scenario for wind generation only is shown and on the bottom of Fig. 21 the mixed generation scenario is shown.

Fig. 22 shows the local power load and generation per local node of a system according to a 22^nd embodiment of the present invention. The corresponding local node power load and power generation of each of the nodes N1 -N10 is shown in Fig. 22 which also shows solar and wind power generation patterns since for the mixed generation the nodes N1 -N5 have solar power generation and the nodes N6-N10 have wind power generation. Figs. 23a-c show the portion of local load served by bought energy from a utility of a system according to a 23^rd embodiment of the present invention for different scenarios.

Figs. 23a-c compare the portion of load served by an external power grid when only intra-node balancing is used, i.e. "baseline", and with collaborative load balancing, i.e. "collaboration" according to an embodiment of the present invention. The results are shown for each of the ten nodes N1 -N10. As it can be seen the decrease at which the portion of load that is dependent on an external power grid energy is reduced and varies for the three different scenarios, i.e. solar generation only, wind generation only and mixed generation. When all nodes are solar-powered the decrease in the amount of energy bought from an external utility/power grid is only low. For example for the local node N7 the balancing performance decreases since the local node N7 has to buy more energy from the utility/external power grid than in the case of only intra-node balancing. This may happen when an energy surplus is lent to another local node though it could be locally stored and later when energy is needed it is not available from other local nodes. In case of the wind powered nodes (Fig. 23b) all local nodes N1 -N10 benefit from collaboration with up to an additional 5% of the load powered from the available local node community renewable energy. The highest improvement in balancing performance is achieved in the mixed generation case (Fig. 23c), in which all local nodes N1 -N10 reduce the amount of energy bought from the external utility/power grid. In this case the actual improvement varies depending on the local node and its type of energy source. The wind-powered local nodes also increase the fraction of load served by local node community renewable energy through collaboration with other local nodes, solar-powered nodes benefit more by increasing this fraction by up to 18%. An even further optimization in balancing performance in this case may be achieved by exploiting the variation in power generation among the local nodes. As Figs. 21 and 22 show, wind energy is sometimes available when solar irradiance is low or the other way round leading to more opportunities for collaboration. Figs. 24a-c show the total transferred power among local nodes of a system according to a 24^th embodiment of the present invention for different scenarios.

Figs. 24a-c show the total power transferred among the local nodes over the simulated period for the three different scenarios. The total amount of generated power currently being routed within the system, i.e. the local node community is therefore shown. This inter-node balancing power is given for the entire simulated period of four weeks. It can be seen that this power is the highest in the mixed- energy case when collaboration leads to the best results. Figs. 25a-c show the energy consumed from storage by local nodes of a system according to a 25^th embodiment of the present invention for different scenarios.

In Figs. 25a-c the energy consumed from local storage for the three different scenarios for conventional intra-node load balancing - "baseline" - and collaborative load balancing - "collaboration" - is shown. Fig. 25a refers to solar generation only for the local nodes, Fig. 25b to wind generation only for the local nodes and Fig. 25c to mixed generation. Figs. 25a-25c show that less energy is consumed from local storage when collaborative load balancing according to an embodiment of the present invention is applied. Hence inter-node balancing according to the embodiments of the present invention can be seen as a virtual expansion of local electricity storage. Since local electricity storage is less frequently used the lifetime of the batteries is increased, since the lifetime is limited by the total number of (charging) cycles.

Figs 26a-c show the number of battery cycles per local node of a system according to a 26^th embodiment of the present invention for different scenarios.

Fig. 26a-26c show for the three different scenarios the number of battery cycles per local node N1 -N10 over a simulated period of four weeks. In respective of the scenario the number of battery cycles is decreased when collaborative load balancing according to an embodiment of the present invention is performed compared with intra-node balancing only (denoted with the term "baseline"). The decrease is achieved even with solar-powered-only local nodes N1 -N10, when collaboration does not substantially improve the use of renewable energy.

Figs. 27a-c show the total energy lent to other local nodes and borrowed from other local nodes per local node of a system according to a 27^th embodiment of the present invention for different scenarios.

Figs. 27a-c show the total energy lent to others and borrowed from others per local node for the three different scenarios. It is shown that in cases of only solar or wind powered nodes all local nodes generate similar amounts of renewable energy and the difference between lent and borrowed energy comes from the difference in power demand, which is shown in Fig. 20. In case of mixed generation, wind-powered nodes produce more energy than the ones that are solar powered leading to higher credit imbalance between the local nodes. A local node's higher credit rating ensures higher priority during a negotiation process when an energy surplus is allocated but in case of severe differences in the number of credits inter-user agreements can be made to compensate for the difference.

Figs. 28a-c show the portion of load served by utility energy with different balancing schemes including balancing schemes of a method according to a 28^th embodiment of the present invention for different scenarios.

In Figs. 28a-c the portion of load served by external utility/power grid energy with different balancing schemes for the three different scenarios is shown. Fig. 28a-c show that only sharing of energy among local nodes, i.e. inter-node balancing without energy storage (intra-node balancing) improves substantially the performance obtained when no balancing is applied, for example when renewable energy is used when and where it is generated. Figs. 28a-c give the portion of load that has to be served from energy bought from the utility/external power grid under the three different balancing scenarios. The term "baseline" without storage refers to the case without balancing, in which energy is used only within the local node in which it was generated and at the time of generation. The term "baseline with storage" is the balancing case considered before, in which local nodes perform intra-node balancing via an 6kWh, 2kW electricity storage. In case of "collaboration without storage" according to an embodiment of the present invention without storage, only inter-node balancing is performed. Finally, "collaboration with storage" corresponds to a combination of intra- and inter-node balancing according to an embodiment of a method and system of the present invention.

Figs. 29a-c show a comparison between a conventional load balancing method and methods according to a 29^th and 30^th embodiment of the present invention for different scenarios. In the previous figures 19-28 it has been assumed that local nodes first offer energy surplus to others and then if there is any energy left the energy can be stored in their own local storage. However, on the other local nodes needing energy additionally to the current generation first try to use local energy storage and if it is not sufficient they request energy from other local nodes. Therefore, Fig. 29a-c show the balancing performance according to an embodiment of the present invention for the three different scenarios if during an energy excess local nodes first store as much energy as possible and only then offer the rest to others which is denoted with the term "modification A". This ensures that a local node cannot perform worse because of the collaboration as it was previously the case with node N7 in the scenario with solar energy in Fig. 23a.

Figs. 30a-c show a comparison between a conventional load balancing method and methods according to a 31^st and 32^nd embodiment of the present invention for different scenarios and Figs. 31 a-c show a comparison of the number of battery cycles for a conventional load balancing method and methods according to the 31 ^st and 32^nd embodiment of the present invention for the different scenarios.

A further modification is shown in Figs. 30a-c for the three different scenarios. Figs. 30a-c are based on a collaboration in which an energy excess or deficit of a local node is first balanced through the network of the local nodes within the local node community and only after performing the inter-node-balancing the local storage is used. Figs. 30a-c show that this modification, termed "modification B", provides small balancing improvements for some local nodes, while others may experience slightly worse performance. Nevertheless, Figs. 31a-c show that the number of battery cycles is further reduced for the three different scenarios increasing the lifetime of the batteries.

In summary the present invention enables a community virtual storage system based on inter-neighbor-relationships for the local node community's energy sharing scheme, while enforcing energy efficiency in the local community grid by collaborative load balancing. The present invention further enables energy routing driven by balancing needs of local supply and demand and sharing history, preferably based on credits. Sharing may be influenced by sharing preferences, for example local storage versus virtual storage at other local nodes.

The present invention further enables load balancing realized via a virtual storage system based on collaboration of local nodes from the same local node community through inter-neighbor credit-based lending schemes. The present invention avoids interactions with an external utility/power grid enabling high utilization of local fluctuating energy sources. The present invention further enables virtual expansion of the energy storage units and a longer life time of energy storages, preferably in form of batteries.

In summary the present invention has inter alia the following advantages: Locally generated energy can be distributed among local nodes within the local node community resulting in a higher local utilization without the need to sell excessing energy if applicable or to buy from a utility or an external power grid. Further the present invention has positive effects on the health of energy storage such as a longer battery lifetime resulting in decreased investment costs. Furthermore, an energy surplus that cannot be stored due to storage limitation can be shared within the local node community using the local node community network as a virtual storage extension of the local physical storage within the local node. The present invention further provides a crediting system controlling that the energy is returned once it is available and the lending local node is in needs. It is further enabled that the storage system may be virtually expanded for the local nodes with small storage. Even further the present invention provides a direct involvement of the local community members in the way of interacting towards the local node community. The present invention enables the realization of a concept based on "lending an energy share to the community common pool". Inter-neighbor lending schemes are provided enabling to raise community member awareness and willingness into sharing with realizing a direct sharing "face". Additionally, the present invention enables a realization of different local market place business models over the design based on a common pool.

Many modifications and other embodiments of the invention set forth herein will come to mind the one skilled in the art to which the invention pertains having the benefit of the teachings presented in the foregoing description and the associated drawings. Therefore, it is to be understood that the invention is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

C l a i m s

1. A method for load balancing between entities (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b 6b) in a local node community (1 ), wherein a plurality of nodes (2, 3, 4, 5, 6) and at least one storing means (2b, 3b, 4b, 5b, 6b) for storing energy forms the local node community (1 ) and wherein a local node (2, 3, 4, 5, 6) comprises generation means (2a, 3a, 4a, 5a, 6a) for generating energy and load means (2c, 3c, 4c, 5c, 6c) for consuming energy, and wherein the local nodes (2, 3, 4, 5, 6) and said storing means (2b, 3b, 4b, 5b, 6b) are connected with each other for exchanging energy,

characterized in that

the energy within the local node community (1 ) for load balancing is assigned to be transferred from one or more local nodes (2, 3, 4, 5, 6) and/or from the storing means (2b, 3b, 4b, 5b, 6b) offering energy to the local node community (1 ) to one or more local nodes (2, 3, 4, 5, 6) and/or to the storing means (2b, 3b, 4b, 5b, 6b) having an energy demand and that the said assigned energy is assigned to be at least partially transferred back later to the one or more entities (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b 6b) having offered the energy.

2. The method according to claim 1 , characterized in that the local node community (1 ) is connected to a utility, preferably a power grid (10), so that when after assigning all offered energy within the local node community (1 ) a total energy surplus or a total energy demand of the local node community (1 ) is determined the respective difference in energy is assigned to be exchanged with the utility (10) to minimize the total energy surplus or energy demand of the local node community (1 ).

3. The method according to one of the claims 1 -2, characterized in that for determining the energy to be offered or to be demanded entity surplus information is provided by the corresponding entity (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b 6b) based on the received energy from the local node community (1 ) and/or the utility (10) and the provided energy to the local node community (1 ) and/or the utility (10).

4. The method according to one of the claims 1 -3, characterized in that entity surplus information is provided in form of positive and negative values indicating a surplus in demand or generation.

5. The method according to one of the claims 1-4, characterized in that actual and historic entity energy surplus and/or demand data is included in the entity surplus information.

6. The method according to one of the claims 1 -5, characterized in that assigning of the energy is based on one or more global energy sharing preferences and/or entity energy sharing preferences.

7. The method according to claims 1 -6, characterized in that assigning of the energy is based on one or more priority policies, preferably which are based on an entity energy sharing history.

8. The method according to one of the claims 1 -7, characterized in that storing means (2b, 3b, 4b, 5b, 6b) are included into one or more local nodes of the local node community (1 ), preferably in each local node (2, 3, 4, 5, 6).

9. The method according to one of the claims 1 -8, characterized in that a charging and/or discharging rate and/or the state of charge of the storing means (2b, 3b, 4b, 5b, 6b) are included in the entity surplus information.

10. The method according to one of the claims 1 -9, characterized in that the entity surplus information is periodically updated and the assigning of the energy is updated accordingly.

1 1. The method according to one of the claims 1 -10, characterized in that for each pair of entities (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b 6b) in the local node community (1 ) a credit value is assigned to each entity (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b 6b) indicating its actual and/or historic energy imbalance to the other entity (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b 6b) of the pair.

12. The method according to one of the claims 1 -1 1 , characterized in that for assigning the energy, the entities (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b, 6b), preferably the local nodes (2, 3, 4, 5, 6) in the local node community, are sorted according to their current energy imbalance in decreasing order.

13. The method according to one of the claims 1 -12, characterized in that when after load balancing between local entities (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b, 6b) within the local node community (1 ) a positive total surplus in energy of the local node community (1 ) is determined the energy in excess is stored in the one or more local storage means (2b, 3b, 4b, 5b, 6b) within the local node community (1 ).

14. The method according to one of the claims 1 -13, characterized in that energy excess of one or more local nodes (2, 3, 4, 5, 6) before or after load balancing with other entities in the local node community is stored in each local node (2, 3, 4, 5, 6) having a respective storage capacity.

15. A system for load balancing between entities (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b, 6b) in a local node community (1 ), wherein a plurality of nodes (2, 3, 4, 5, 6) and at least one storing means (2b, 3b, 4b, 5b, 6b) for storing energy forms the local node community (1 ) and wherein a local node (2, 3, 4, 5, 6) comprises generation means (2a, 3a, 4a, 5a, 6a) for generating energy and load means (2c, 3c, 4c, 5c, 6c) for consuming energy, and wherein the local nodes (2, 3, 4, 5, 6) and said storing means (2b, 3b, 4b, 5b, 6b) are connected with each other for exchanging energy, preferably for performing with a method according to one of the claims 1 - 14,

characterized by

controlling means (CM) operable to assign the energy within the local node community (1 ) for load balancing to be transferred from one or more local nodes (2, 3, 4, 5, 6) and/or from the storing means (2b, 3b, 4b, 5b, 6b) offering energy to the local node community (1 ) to one or more local nodes (2, 3, 4, 5, 6) and/or to the storing means (2b, 3b, 4b, 5b, 6b) having an energy demand, and to assign an at least partially back transfer later to the one or more entities (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b, 6b) having offered the energy.

16. Controlling means for controlling load balancing between entities (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b, 6b) in a local node community (1 ), wherein a plurality of nodes (2, 3, 4, 5, 6) and at least one storing means (2b, 3b, 4b, 5b, 6b) for storing energy forms the local node community (1 ) and wherein a local node (2, 3, 4, 5, 6) comprises generation means (2a, 3a, 4a, 5a, 6a) for generating energy and load means (2c, 3c, 4c, 5c, 6c) for consuming energy, and wherein the local nodes (2, 3, 4, 5, 6) and said storing means (2b, 3b, 4b, 5b, 6b) are connected with each other for exchanging energy, preferably for performing with a method according to one of the claims 1 -14, and/or a system according to claim 15, operable to assign the energy within the local node community (1 ) for load balancing to be transferred from one or more local nodes (2, 3, 4, 5, 6) and/or from the storing means (2b, 3b, 4b, 5b, 6b) offering energy to the local node community (1) to one or more local nodes (2, 3, 4, 5, 6) and/or to the storing means (2b, 3b, 4b, 5b, 6b) having an energy demand, and to assign an at least partially back transfer later to the one or more entities (2, 3, 4, 5, 6, 2b, 3b, 4b, 5b, 6b) having offered the energy.