GB2546795A

GB2546795A - Demand-side management method and system

Info

Publication number: GB2546795A
Application number: GB1601697.4A
Authority: GB
Inventors: Sasaki Hiroto
Original assignee: Hitachi Ltd
Current assignee: Hitachi Ltd
Priority date: 2016-01-29
Filing date: 2016-01-29
Publication date: 2017-08-02
Also published as: JP6293935B2; GB201601697D0; JP2017135976A

Abstract

A method of, or system for, operating a plurality of distributed energy resources (DERs) electrically connected to a power supply network. The DERs are operated according to an operating plan that specifies times when particular DERs will draw electricity from the network, taking into account demand side constraints for operation of the DERs and predetermined operating limits of the supply network. A plurality of operation plans may be iteratively generated where scheduled operation of at least one of the DERs varies between the plurality of plans, then a score is calculated for each operation plan in relation to a model of the power supply network which indicates the relative ease with which the supply of electricity can be maintained within the predetermined operating limits of the network under that operation plan. The DERs are then operated according to the best scored plan. Predicted voltage drops and network load factors may be used in the score calculations.

Description

DEMAND-SIDE MANAGEMENT METHOD AND SYSTEM Field of the Invention

The present invention relates to management of distributed energy resources (DERs) in a power supply network, such as an electricity supply network. In particular, the present invention is related to providing optimized demand side response (DSR) in such a network.

Background

In power supply networks, the power flow structure consists of generation, transmission, distribution and demand side in a sequence.

To try to provide a more efficient network which does not waste assets and energy, the supply and demand should ideally be matched. Traditionally, this has been achieved by mainly managing the supply-side to try to match the demand. However, it is expensive to manage only the supply side to match the demand. Further, to avoid blackouts or brownouts in the network, the supply capacity is usually managed to exceed the peak demand.

However, amongst other things, this leads to loss of natural resources and energy in the network, which is both expensive and provides an unnecessary impact on the environment by increasing the environmental burden such as the carbon footprint for each unit of demanded (used) electricity/energy.

In recent years, the demand side has also been managed, for example by allowing Distributed Energy Resources (DERs) within the network to be managed in real time so that the time at which they consume electricity is changed, or even the way in which they consume electricity is changed. Although, of course, the user does not necessarily need to change the time or way in which they utilise the energy; for example, only the time at which the DERs store the electrical energy may change. Such management is often referred to as Demand Side Response (DSR). For example, DERs can provide battery storage, or other forms of electricity storage, to the network so that energy can be stored when it is in plentiful supply or perhaps when there is too much for the network to carry. This stored electricity can then be returned to the network when it is needed, and/or when the network is able to carry it. Indeed, the DSR can also take account of low carbon electricity generated locally, e.g. domestically via a rooftop solar panel, to help reduce the assets capacity and energy losses involved in transporting the generated electricity across the network.

Ultimately, by optimizing the DSR, the power supply network can be managed to operate in a more efficient manner, thereby reducing the losses in the network and reducing the overall environmental impact on the generation of energy to meet demand. For instance, in a simplified example, if electricity generated from e.g. wind generators were to fall off due to unfavourable weather conditions, then rather than needing to turn on or increase the output of a fossil fuel generator (e.g. a coal or gas fired power station), DSR can be used to bridge the gap in real time between supply and demand by making adjustments to the electricity consumption.

Furthermore, in the UK, for example, the power supply network is managed by horizontally specialized different companies, such as the transmission system operator (TSO), the distribution network operator (DNO) and so on. Because it is difficult to accumulate electricity, amounts of the demand and the supply are balanced by the TSO as the system operator. The TSO controls the supply and/or demand in real time within procured capacity. The reserve capacity is provided by the generators and aggregators. Because of the increased use of intermittent renewables in the power supply network, the need to manage the supply and demand (and therefore the reserve capacity) is increasing.

The aggregator is a relative new comer to the power supply market. Aggregators aggregate Distributed Energy Resources (DERs) to create capacity within the network (and indeed electricity). At the beginning, DERs include devices such as small hydro power plants and emergency backup generators. However, because of the evolution of Information Communication Technology (ICT), DERs controllable by e.g. an aggregator are expanding from distributed generators, which now include domestic solar panels, to further include energy storage resources such as electric vehicles (i.e. the large capacity batteries therein) and heat pumps which can provide an enlarged reserve capacity within the network.

On the other hand, the DNOs have responsibility to operate the network within distribution network constraints, for example by maintaining voltage variation and power flow within statutory limits. But, so far, in the conventional systems the aggregator's DSR services for DNO are limited to providing reactive first-aid treatment (“emergency response”) of the network at stress time (e.g. unusual peak demand).

Further, an aggregator operates by gathering and utilising DERs for electric power network management. However, in horizontally divided business structures it is often the case that each of the DERs is utilised exclusively either for Distribution Management or System Operation; which is, for example, Demand-side Response (DSR) for distribution constraint management at the distribution network operator (DNO) level, or demand-supply balancing management at the transmission system operator (TSO) level.

This exclusive DER utilisation may cause DSR conflicts between DNO level and TSO level, or some DSR for TSO may result in the DNO needing extra DSR for DNO rather than ordinary distribution management, and vice versa.

Further, this exclusive DER utilisation also prevents complete utilisation of DER as available network assets. Some resources for DNO may be taking a rest during DSR for TSO even if they have capability to contribute to the DSR for the TSO, and vice versa. This is inefficient, and can lead to losses in the network resulting in a negative environmental impact as a result of trying to generate sufficient energy to meet the demand. WO2014/186846 discloses a smart grid management system and method. However, according to the disclosure of this document, it is likely that DSR capacity may not be fully applied (optimised) for balancing electricity demand and supply at TSO level.

Summary of the Invention

The present invention provides a method according to claim 1, whereby the DSR influence is controlled and optimised in advance by optimising the DER operation plan which considers the operation of a set of DERs working in a predetermined period of time, often referred to as a DSR window, taking into account each DER operating (i) with and (ii) without a DSR operation schedule.

Accordingly, the present invention aims to provide a method and system for managing the operation of DERs in a power supply network, for example on complicated horizontally divided business structures. The present invention utilises DERs by both ordinary management for DNO and DSR for TSO whilst taking full account of demand-side constraints.

The present invention aims to permit DNOs to utilize DERs which are not owned by them, at ordinary time (when a DSR is not required) and leads DERs to desirable behaviour for as long as is needed within the limits of the demand-side constraint.

As the system is able to optimise e.g. the day-ahead DER operation plan in a harmonious manner with other equipment for distribution network management, the DNO can confirm and control DSR impact on their network in advance of the real-time DSR execution. As a result, the system can avoid conflicts between DNO level network management and conventional DSR execution for TSO. That is to say, the system enables an aggregator to provide effective utilisation of DER for both DNO and TSO.

The step of calculating the score may include the step of evaluating a predicted voltage drop profile at one or more network nodes when executing the generated operation plan.

The step of calculating the score may include the step of evaluating the load factor of the network, or a portion thereof.

The method may further include the steps of evaluating the feasibility of operating on the basis of the DSR schedule of the generated operation plan, and rejecting the operation plan in the event that the feasibility is negative, wherein the feasibility is evaluated to be positive if the reduction in the electricity drawn from the network by the DERs during the predetermined period when operating according to the DSR schedule is determined to be greater than or equal to a desired reduction.

The desired reduction may be determined on the basis of historically required reductions.

The plurality of operation plans may be generated iteratively.

In another aspect, the present invention provides a system (apparatus) for operating a plurality of distributed energy resources (DERs) electrically connected to a power supply network, as set forth in the appended independent system claim.

In calculating the score, the system may be configured to evaluate a predicted voltage drop profile at one or more network nodes when executing the generated operation plan.

In calculating the score, the system may be configured to evaluate the load factor of the network, or a portion thereof.

The system may be further configured to evaluate the feasibility of operating on the basis of the DSR schedule of the generated operation plan, and to reject the operation plan in the event that the feasibility is negative, wherein the feasibility is evaluated to be positive if the reduction in electricity drawn from the network by the DERs during the predetermined period when operating according to the DSR schedule is determined to be greater than or equal to a desired reduction.

The system may be configured to generate the plurality of operation plans iteratively.

Brief Description of the Drawings

Embodiments of the invention will now be described by way of example with reference to the accompanying drawings in which:

Figure 1 shows an embodiment of a system according to an aspect of the present invention; Figure 2 shows a flow chart representative of an embodiment of the present invention;

Figure 3 shows examples a “without DSR” schedule and “with DSR” schedule, providing an optimized DER operation plan generated by a method according to an aspect of the present invention;

Figure 4 shows a flow chart representative of a method for generating an optimized operation plan according to an aspect of the present invention; and

Figure 5 shows an example of a set of demand side constraints for a DER.

Detailed Description and Further Optional Features of the Invention

Figure 1 shows a system architecture of an embodiment of the present invention, in which a power supply network 10 is electrically coupled to one or more distributed energy resources (DERs) 12 by one or more network interfaces 14.

Each DER 12 is able to draw energy, e.g. electricity, from the power supply network 10 via the network interface 14. The network interface may be a power conditioner for example. Typically, the network interface 14 is provided together with the DER 12 and may be owned by a common owner. A communication unit 16 is provided for communicating with the DER 12. The communication unit may be in wireless or wired communication with the DER, for example. The communication unit 16 conveys control signals to the DER, for example. The communication unit 16 may be owned by the DER owner, or by an aggregator for example. A DER management unit 18 is provided to exert control over the operation of the DER. This unit manages DER features such as response characteristics and operational demand-side constraints, and controls DER according to collected DER state information and DER schedules. This unit is usually owned by an aggregator.

An operation unit 20 operates the DER according to a day-ahead DER operation plan “with DSR” and “without DSR” execution (as explained below). The operation is achieved by sending signals overwriting a DER schedule or directly controlling a DER via the DER management unit. This unit ordinarily operates a DER based on a “without DSR” plan. However if there is a DSR trigger, e.g. from the TSO, then the operation unit is able to operate the DER based on the “with DSR” plan. This unit is usually owned by an aggregator. A deal unit 22 is provided to deal capacity (and electricity) via a suitable electricity market and settles DSR operation results with the markets 24. This unit is usually owned by an aggregator.

An account unit 26 is able to calculate potential rewards for the consumer and settles ordinary DER operation for DNO and DSR operation for TSO with consumers.

Various databases (DBs) are provided, such as DER information DB 28, contract DB 30, historical DB 32, network DB 34, and so on. For example, DER information DB 28 contains DER connection point information, contract DB 30 contains contractual DSR amount. Historical DB 32 contains historical electricity consumption data of DER. Network DB 34 contains network topology and/or impedance, and predicted (electricity) demand and/or (electricity) generation at each network node.

An easiness-of-control evaluation unit 36 is provided to evaluate the easiness of appropriate real-time control of distribution network management as network-evaluated scores, for example voltage and capacity evaluated score, by using network topology and/or impedance, predicted demand and/or generation at each network node, DER connection point information, acquired from network DB, network DB and DER information DB, respectively. This unit can be owned by either DNO or an aggregator. A DER-operation-plan optimisation unit 38 is provided to optimise the DER operation plan based on the network-evaluated score and e.g. a DSR feasibility evaluation, subject to demand-side constraints and e.g. contractual DSR amount acquired from DER management unit and contract DB, respectively. This unit can be owned by either a DNO or an aggregator together with the easiness-of-control evaluation unit.

The operation of the above system will now be explained, by describing a method according to an aspect of the present invention.

Figure 2 shows an operation flow for controlling the operation of the DERs coupled to the power network of Figure 1.

In the step S2.1 the DER operation plan optimisation unit creates a DER operation plan for the DERs. The DER operation plan includes an optimised operation schedule for the DERs which is capable of operating the DERs either “with DSR” or “without DSR” execution at any given point in time. Preferably, the DER operation plan is created a day ahead of its intended implementation, thus the created (generated) DER operation plan is preferably a day-ahead DER operation plan.

The creation of the optimized DER operation plan is explained below in more detail with reference to Figure 4.

After the optimized DER operation plan is created, it is used to control the operation of the DERs coupled to the network. The DERs are operated according to the optimized DER operation plan. So, typically, the DERs are initially operated according to the “without DSR” plan or schedule. If a DSR order is received, then the DERs are controlled to operate according to the “with DSR” plan or schedule instead.

Thus, as shown in step S2.2 the operation unit repeats steps S2.3 to 2.5 in a controlled cyclic manner, for example every 30 minutes, on the operation day. If the operation unit receives a DSR order (instruction), e.g. from the TSO either directly or indirectly, then in step S2.3 the operation unit operates the DER based on the “with DSR” schedule in step S.4. Otherwise the operation unit continues to operate the DERs based on the “without DSR” schedule in step S2.5. At the end of the duration of the operation plan the process stops at S2.6. The process is then repeated from S2.1 for the next period of time covered by the operation plan. Typically, each operation plan covers a 24 hour period.

Because of this DER operation flow, the TSO for example can effectively utilize the DERs for DSR, and e.g. the DNO can know the influence range of the DSR in advance according to the optimized day-ahead DER operation plan created in step S2.1. A sample of a day-ahead DER operation plan comprising a “with DSR” execution schedule and a “without DSR” execution schedule is shown in Figure 3. As mentioned, the way in which these schedules are generated according to the present invention will be described later with reference to Figure 4.

However, as will be seen, for each DER (DER1, DER2...DERn), the operation mode is set for each control cycle. A control cycle is a period of time during which the DER is operated according to a specific mode. For example, in Fig. 3 the control cycle is one hour long. The control cycle can be any predetermined length of time (a predetermined period of time).

So, for example, in the “without DSR” schedule at 1600-1700, DER1 will be operated according to the ordinary storage mode, where DER1 is controlled to store energy (e.g. by storing electricity itself or by storing e.g. generated heat). Whereas at 1800-1900 DER1 is operated according to the consumption mode where DER1 is operated to consume energy, but not necessarily store the energy. Furthermore, during consumption mode, the DER might only use the stored energy rather than drawing electricity from the power supply network. (However, the DER might nonetheless draw electricity from the power supply network during consumption mode, for example to supplement the amount of stored energy.)

The designation of the consumption mode and the storage mode, for example, and the initial scheduling of such modes represent the demand side constraints. In particular, whilst both the storage mode and the consumption mode consume energy, consumption mode can be distinguished from the storage mode in that the consumption mode is fixed to occur at a particular time by the user of the DER (the consumption mode cannot be rescheduled by another entity, e.g. the DNO or TSO for example), whereas the storage mode is able to be rescheduled by another entity such as the DNO or TSO for example. Thus, the demand side constraints include first scheduling elements (e.g. consumption mode) which cannot be rescheduled, and second scheduling elements (e.g. storage mode) which can be rescheduled.

In particular, when creating the “with DSR” schedule, the consumption mode cannot be rescheduled with respect to the “without DSR” schedule; whereas the storage mode can be rescheduled in the “with DSR” schedule with respect to the “without DSR” schedule.

Preferably, a “DSR window” is identified as being a typical time during which DSR may need to be executed for the system operation. Therefore, when creating the “with DSR” schedule, the storage mode elements of the schedule are rescheduled from the control cycles within the “DSR window” to control cycles outside the “DSR window”. When the storage mode elements of the schedule are rescheduled in the “with DSR” schedule relative to the “without DSR” schedule, they can be referred to as DSR storage mode elements.

For example, referring to Fig. 3, if we assume a heat pump water heater is a controlled DER, then values of DER operation mode can be identified as consumption mode, ordinary storage mode and DSR storage mode. Because heat pumps draw electricity from the power supply network during the ordinary storage mode and because the storage mode elements are reschedulable, some (or possibly all) of the ordinary storage mode elements are rescheduled in the “with DSR” schedule relative to the “without DSR” schedule to be DSR storage mode elements and are thus moved out of the DSR window time.

Therefore, if the DERs are instructed to switch from the “without DSR” schedule to the “with DSR” schedule, then e.g. DER1 and DER2 will not store energy between 1700-1800, but instead will store energy at 2100-2200, thereby reducing the consumption of electricity by DER1 and DER2 during the DSR window.

The instruction to switch to the “with DSR” schedule may be made in response to a TSO’s DSR signal. For example, as shown in Fig. 3 a warning from TSO may be issued before DSR window, and an instruction is issued to the DERs to switch their operation schedule from the “without DSR” schedule to the “with DSR” schedule.

Accordingly, the operation plan comprising the “without DSR” and “with DSR” schedules is created in advance. For example, the schedules may be created a day ahead. The present invention provides a method for optimizing the operation plan, so that the “without DSR” and “with DSR” schedules are optimized to allow the power supply network to be operated efficiently to achieve the benefits described herein. PER operation plan optimization

Now the method for generating an optimized DER operation plan will be described with reference to the example flow chart of Fig. 4. The generation of the optimized DER operation plan is performed at step S2.1 in Figure 2.

It should be noted that the generation of the optimized DER operation plan is described to include optional business focussed features (in particular, references are made to contractual obligations and “market rules” for example). Whilst it may be desirable to include such features in a real world commercial embodiment of the present invention, such features are not necessary to achieve the technical effect of the present invention, and importantly they do not detract from the technical effect provided by the present invention.

In step S4.1 of Fig. 4 the DER-operation-plan optimisation unit obtains demand-side constraints on the operation of the DERs from the DER management unit; obtains the operational constraints on the power supply network; and optionally obtains the contractual DSR amount which the operator has agreed to provide from the contract DB.

In step S4.2, the DER-operation-plan optimisation unit optionally calculates a DSR baseline using historical consumption data of the DERs provided in the historical DB, and based on the DSR market rules.

Step S4.3 is used to show that the DER-operation-plan optimisation unit repeats steps S4.4 to S4.9, e.g. in an iterative manner, to generate the optimized (e.g. day-ahead) DER operation plan.

Based on the obtained demand-side constraints, in an embodiment of the invention, in a first iteration a DER operation plan is generated by creating a “without DSR” schedule and a “with DSR” schedule in which reschedulable elements are randomly scheduled (or rescheduled). In the other iterations, all possible versions of the “with DSR” and “without DSR” schedules are exhaustively explored (within the limitations of the demand side constraints). This can be done for example by using stored sets of schedules and objective function scores generated in step S4.8 executed in preceding iteration(s), as will be discussed below. This exploration can be done by using machine leaning and/or optimisation methods such as mixed integer programming.

In step S4.5, the easiness-of-control evaluation unit evaluates the easiness of suitable realtime control of the distribution network management as a network-evaluated score. In other words, based on the generated “without DSR” schedule and a “with DSR” schedule forming a DER operation plan, and based on network constraints obtained at step S4.1, a score is generated to indicate how easy it would be to operate the network according to the generated schedules and maintain the operation of the network within the obtained network constraints (e.g. statutory limits).

In optional step S4.6, the DER-operation-plan optimisation unit evaluates DSR feasibility at each control cycle in DSR window time by using such information as the DSR baseline obtained at step S4.2, the contractual DSR amount obtained at step S4.1, and the “with DSR” and the “without DSR” total demand.

In one example, if we take the heat pump water heater as a controlled DER again, and if we say that the heat pump water heater only draws electricity from the power supply network during storage mode, then the “with DSR” total demand for that DER is calculated as total demand of ordinary storage mode for the DER at each control cycle in the DSR window in the “with DSR” schedule in Fig. 3. The total demand for all such DERs can then be summed accordingly.

In this example, the DSR feasibility is set as positive if a desired (e.g. contractual) DSR amount subtracted from a DSR baseline (for example a historically typical DSR total demand) is larger than the “with DSR” total demand.

The desired DSR amount might be an amount of DSR to sell on the markets, for example. However, the present invention is not so limited. The desired DSR amount might be an amount of DSR to provide a buffer to account for an unusual drop in the supply of energy from intermittent energy resources such as wind power, for example. Thus the desired DSR amount can be used to postpone the drawing of electricity from a power supply network to a later time, when electricity is available to be drawn down and thereby contribute to a more efficient use of the available energy. The DSR baseline is a historically typical DSR total demand and is usually predicted or estimated by using historical demand data.

In optional step S4.7, if the evaluated DSR feasibility at step S4.6 is positive then the process proceeds to optional step S4.8. Whereas, if the evaluated DSR feasibility at step S4.6 is not positive, the DER-operation-plan optimisation unit returns to the beginning of the loop at step S4.3 and another iteration is performed, whereby a respectively different operation plan (having different “without DSR” and/or “with DSR” schedules) is generated and evaluated in steps S4.4 to S4.7.

In step S4.8, the DER-operation-plan optimisation unit calculates an objective function score using the network-evaluated score at the step S4.5. This objective function may simply be the network-evaluated score itself. Or it may be the network-evaluated score combined with any other score indicating the quality of the DER operation plan in the respective iteration, as discussed below.

In step S4.9, the DER-operation-plan optimisation unit preferably temporarily saves the DER operation plan and objective function score in association with one another.

Steps S4.3 to S4.9 are then repeated for every possible “with DSR” and “without DSR” schedule for each DER in the network, so that an optimal operation plan for the DER operation is found.

In step S4.10, the DER-operation-plan optimisation unit selects the DER operation plan to be used on the basis of the saved objective function score. The best objective function score is used to choose the DER operation to be used for the target period of time, e.g. for the next day.

Thus, according to an aspect of the present invention, an optimized DER operation plan for use in the future (e.g. the next day) is generated, to include “without DSR” and “with DSR” schedules which provide efficient use of the supply network.

Fig. 5 shows an example of demand-side constraints for an example DER such as a heat pump water heater. The demand-side constraint is typically set by the consumer via user interface of DER management unit. In the example, the user sets a time for each section including the consumption mode, which is set to 0700-0900 (similar to the consumption mode set to a different time in Figure 3). The necessary thermal storage amount is also provided in each section as a demand setting.

For example, the storage mode duration can be set as a demand side constraint (shown as 2 hours in Fig. 5). Similarly to the ordinary storage mode in Figure 3, the storage mode can be rescheduled to another time, whereas the consumption time cannot be rescheduled.

Thus, the demand side constraints in Fig. 5 specify that for the time period 0000 (i.e. midnight) to 0900, the DER will be operated in consumption mode between 0700 and 0900, and the DER can be operated in storage mode anytime between 0000 and 0700 for 2 hours. Thus, when generating the “without DSR” and “with DSR” schedules at step S4.4, the ordinary storage mode and the DSR storage mode can be located at any time during 0000 to 0700 so long as the DER is operated in storage mode for 2 hours during that period of time, and so long as the consumption mode is not rescheduled from 0700 to 0900.

As will also be seen, another possible demand side constraint is that the DER is prohibited from being operated in one or more particular modes during a set period. So, Fig. 5 shows that from 2200 to 0000 there is set a prohibit time, prohibiting a storage operation during this period. Other demand side constraints are possible.

These demand-side constraint settings enable the presented system to minimise any adverse influence of the generated DER operation plan, including DSR execution, on the consumer's DER usage.

Easiness of real-time control of network

Here is discussed the evaluated easiness of suitable real-time control of the distribution network management, which is performed at step S4.5 in Figure 4.

As an example, voltage and/or capacity evaluated scores will be explained as follows, however the present invention is not necessarily limited to these examples.

As a voltage evaluated score, the present invention can use the shape of a voltage drop profile defined as below. The smaller this voltage-evaluated score is, the larger the voltage control margin is available, and the easier the real-time control of the network is.

where for each node k in the network, at time t, node voltage Vk,t is calculated by voltage drop calculation using load lk,t, generation mu, and network impedance. The score corresponds to a ratio of voltage difference in a feeder to statutory range Viow - VUpp.

If multiple feeders are connected to the same bank and use shared voltage control at the bank, then it is possible to use the voltage difference in the feeders for the score calculation. In practice, the DNO has to keep real-time voltage values within statutory limits and they control the voltage using conventional voltage control equipment on their distribution network such as transformers, Step Voltage Regulator and so on, as is known in the art. When the evaluated score is favourable, it indicates that the DNO can more easily control the supply network to maintain supply within the network constraints such as the statutory limits.

As a capacity evaluated score the presented system can use the so-called load factor. The load factor is defined as the average power divided by the peak power over a period of time. Keeping this score high leads to reduction of the capacity needed for DER operation with and “without DSR” execution, because it makes the peak power low against the load amount. And because high load factor imposes efficient utilization of the network capacity, the DNO can improve their asset value due to dispatch load on vacant time of the asset.

Other comments

As mentioned above, the present invention mitigates for network constraints when generating an operation plan for future DER operation both “with DSR” and “without DSR” execution, and thus provides an optimal operation plan.

Additionally, for DNOs the present invention also reduces uncertainty of real-time load amount and allows the DNO to control the network management more easily and more precisely.

From a practical point of view, if the DNO owns the DER-operation-plan optimisation unit and the easiness-of-control evaluation unit, the aggregator does not even need to know the network topology and/or impedance, predicted demand and/or generation at each network node, or DER connection point information but can simply receive the optimized day-ahead operation plan from DER-operation-plan optimisation unit. This simplifies the management of the power supply network.

Further, the DNO can also easily enhance the same system to manage many aggregators' assets connected to the DNO distribution network in the same manner as presented above, thereby allowing for an easily scalable optimally managed power supply network.

Claims

1. A method of operating a plurality of distributed energy resources (DERs) electrically connected to a power supply network, the method including: operating the DERs according to an operation plan comprising an ordinary schedule which includes scheduling elements resulting in the DERs operating to draw electricity from the power supply network at specified times, wherein the scheduling elements include (i) first scheduling elements associated with DER operation modes which are permitted to be rescheduled, and (ii) second scheduling elements associated with DER operation modes which are not permitted to be rescheduled, and a demand side response schedule (DSR schedule) which includes the scheduling elements, where the first scheduling elements are rescheduled within the DSR schedule relative to the ordinary schedule, so as to provide a demand side response from the DERs whereby electricity drawn from the power supply network by the DERs is reduced within a predetermined period of time relative to that which would be drawn by the DERs operating according to the ordinary schedule during the predetermined period of time, wherein the DERs are controllable to change their operation schedule from the ordinary schedule to the DSR schedule; the method further including the steps of: obtaining, for the DERs, predetermined demand side constraints for determining the first scheduling elements; obtaining network information about predetermined operating limits of the power supply network; generating a plurality operation plans, each including an ordinary schedule and a DSR schedule, wherein in each operation plan at least one of the determined first scheduling elements is scheduled differently within the ordinary schedule and/or the DSR schedule with respect to the other generated operation plans, and wherein in each iteration (a) a score is calculated based on the generated operation plan, on a model of the power supply network and on the obtained network information, whereby the score indicates the relative ease with which the supply of electricity can be maintained within the predetermined operating limits when the DERs are operated according to the generated operation plan, (b) storing the calculated score in association with the generated operation plan; selecting the generated operation plan with the best score; operating the DERs according to the selected operation plan.

2. A method according to claim 1 wherein the step of calculating the score includes the step of evaluating a predicted voltage drop profile at one or more network nodes when executing the generated operation plan.

3. A method according to any one of claims 1 to 2 wherein the step of calculating the score includes the step of evaluating the load factor of the network, or a portion thereof.

4. A method according to any one of claims 1 to 3 wherein the method further includes the steps of evaluating the feasibility of operating on the basis of the DSR schedule of the generated operation plan, and rejecting the operation plan in the event that the feasibility is negative, wherein the feasibility is evaluated to be positive if the reduction in the electricity drawn from the network by the DERs during the predetermined period when operating according to the DSR schedule is determined to be greater than or equal to a desired reduction.

5. A method according to claim 4 wherein the desired reduction is determined on the basis of historically required reductions.

6. A method according to any one of claims 1 to 5 wherein the plurality of operation plans are generated iteratively.

7. A system for operating a plurality of distributed energy resources (DERs) electrically connected to a power supply network, the DERs being operable according to an operation plan comprising an ordinary schedule which includes scheduling elements resulting in the DERs operating to draw electricity from the power supply network at specified times, wherein the scheduling elements include (i) first scheduling elements associated with DER operation modes which are permitted to be rescheduled, and (ii) second scheduling elements associated with DER operation modes which are not permitted to be rescheduled, and a demand side response schedule (DSR schedule) which includes the scheduling elements, where the first scheduling elements are rescheduled within the DSR schedule relative to the ordinary schedule, so as to provide a demand side response from the DERs whereby electricity drawn from the power supply network by the DERs is reduced within a predetermined period of time relative to that which would be drawn by the DERs operating according to the ordinary schedule during the predetermined period of time, wherein the DERs are controllable to change their operation schedule from the ordinary schedule to the DSR schedule; the system being configured to obtain, for the DERs, predetermined demand side constraints for determining the first scheduling elements; obtain network information about predetermined operating limits of the power supply network; generate a plurality operation plans, each including an ordinary schedule and a DSR schedule, wherein in each generated operation plan at least one of the determined first scheduling elements is scheduled differently within the ordinary schedule and/or the DSR schedule with respect to the other generated operation plans, and wherein in each iteration (c) a score is calculated based on the generated operation plan, on a model of the power supply network and on the obtained network information, whereby the score indicates the relative ease with which the supply of electricity can be maintained within the predetermined operating limits when the DERs are operated according to the generated operation plan, (d) storing the calculated score in association with the generated operation plan; select the generated operation plan with the best score; operate the DERs according to the selected operation plan.

8. A system according to claim 7 wherein in calculating the score the system is configured to evaluate a predicted voltage drop profile at one or more network nodes when executing the generated operation plan.

9. A system according to any one of claims 7 and 8 wherein in calculating the score, the system is configured to evaluate the load factor of the network, or a portion thereof.

10. A system according to any one of claims 7 to 9 wherein the system is further configured to evaluate the feasibility of operating on the basis of the DSR schedule of the generated operation plan, and to reject the operation plan in the event that the feasibility is negative, wherein the feasibility is evaluated to be positive if the reduction in electricity drawn from the network by the DERs during the predetermined period when operating according to the DSR schedule is determined to be greater than or equal to a desired reduction.

11. A system according to claim 10 wherein the desired reduction is determined on the basis of stored historically required reductions.

12. A system according to any one of claims 7 to 11 wherein the system is configured to generate the plurality of operation plans iteratively.