Power management of a hybrid power plant

Abstract

A method of power management of a hybrid power plant includes at least one type of renewable power generation equipment and an energy storage system, including: before a first dispatching time range: using first forecasts of energy production capability and energy price, and in particular hybrid plant status and plant parameters, to derive a first power generation schedule defining power generation in the first dispatching time range; at a dispatch point in time within the first dispatching time range: using second forecasts of energy production capability and energy price, and in particular plant status and plant parameters, to derive a second power generation schedule defining power generation in a second dispatching time range; and calculating an optimal power injection value for the dispatch point in time based on the first power generation schedule and the second power generation schedule taking into account at least one constraint.

Claims

1. A method of power management of a hybrid power plant using a processor and having at least one type of renewable power generation equipment and an energy storage system for storing power for a utility grid, comprising: before a first dispatching time range: to derive, using first forecasts of energy production capability and energy price, a first power generation schedule defining power generation in the first dispatching time range; at a dispatch point in time within the first dispatching time range: using second forecasts of energy production capability and energy price, comprising plant status and/or plant parameters, to derive a second power generation schedule defining power generation in a second dispatching time range; calculating an optimal power injection value for the dispatch point in time based on the first power generation schedule and the second power generation schedule taking into account at least one constraint, wherein the first power generation schedule and the second power generation schedule determines an amount of hybrid plant power output communicated to a point of common connection to which the at least one power generation equipment and the energy storage system is connected; and injecting the hybrid plant power output produced by the at least one type of renewable power generation equipment and stored by the energy storage system into the utility grid at the point of common connection according to the optimal power injection value.

2. The method according to claim 1, wherein the constraint comprises a power threshold, wherein the optimal power injection value is calculated such that a deviation between the optimal power injection value and a power value according to the first generation schedule is below the power threshold, wherein the power threshold is a relative power threshold, defining a maximally allowable deviation.

3. The method according to claim 1, wherein calculating the optimal power injection value comprises finding an extreme of an objective function subject to the constraint.

4. The method according to claim 3, wherein the objective function and/or the at least one constraint is designed to achieve at least one of: a deviation mitigation, thereby applying capacity firming; a technical optimization, thereby applying capacity firming; an economic optimization, thereby applying energy arbitrage and/or time shifting, wherein price differences at different time ranges and/or for different types of energy are exploited for profit maximization.

5. The method according to claim 3, calculating the optimal power injection value is performed using a model including the objective function, wherein the model is composed of continuous variables with a linear characteristic; wherein the objective function of the model and a set of linear constraints define a convex solution space including the optimal power injection value, wherein the optimal power injection value is an extreme of the convex solution space.

6. The method according to claim 1, wherein the constraint comprises at least one of the following: at least one hybrid plant requirement; at least one hybrid plant maximum power injection capability; a charging and/or discharging capability of the storage system; an allowed charging and/or discharging level of the storage system.

7. The method according to claim 1, wherein the first power generation schedule and the second power generation schedule overlap in time or do not overlap in time.

8. The method according to claim 1, wherein the optimal power injection value is calculated further considering actual energy production capability and/or actual energy price and/or actual hybrid plant status.

9. The method according to claim 1, wherein the first and/or second dispatching time range has a duration of between 1 hours and 48 hours; and/or wherein the optimal power injection value for subsequent dispatch points in time is calculated every 5 min to 60 min repetitively.

10. The method according to claim 1, wherein the forecasts of energy production capability and/or energy price are obtained from an external source and/or include forecasts regarding at least one of: power due to wind; power due to sun irradiance; power due to tidal; plant power; energy price of wind energy; energy price of solar energy; energy price of tidal energy; energy price of any type of energy; any other energy generation or consumption.

11. The method according to claim 1, wherein the first and/or the second power generation schedule is derived such as to maximize plant power output and/or to minimize usage of the energy stored in the storage system and/or to maximize profit of selling the generated energy; and/or wherein the hybrid plant comprises at least one wind and/or at least one solar energy converting unit.

12. An arrangement for power management of a hybrid power plant comprising at least one type of renewable power generation equipment and an energy storage system, the arrangement comprising a processor adapted: before a first dispatching time range: to derive, using first forecasts of energy production capability and energy price, a first power generation schedule defining power generation in the first dispatching time range; at a dispatch point in time within the first dispatching time range: to derive, using second forecasts of energy production capability and energy price, comprising plant status and/or plant parameters, a second power generation schedule defining power generation in a second dispatching time range; and to calculate an optimal power injection value for the dispatch point in time based on the first power generation schedule and the second power generation schedule taking into account at least one constraint, wherein the arrangement is adapted to carry out or control the method according to claim 1.

13. A hybrid power plant system, comprising: at least one type of renewable power generation equipment; an energy storage system; and the arrangement according to claim 12.

14. The method according to claim 1, wherein the at least one hybrid power plant comprises a wind park.

15. The method of claim 11, wherein the at least one solar energy converting unit comprises a solar park and/or tidal energy converting equipment.

Description

BRIEF DESCRIPTION

(1) Some of the embodiments will be described in detail, with reference to the following figures, wherein like designations denote like members, wherein:

(2) FIG. 1 schematically illustrates a hybrid power plant system including an arrangement for power management of a hybrid power plant according to an embodiment of the present invention, performing a method of power management according to an embodiment of the present invention;

(3) FIG. 2 schematically illustrates an arrangement for managing a hybrid power plant according to an embodiment of the present invention;

(4) FIG. 3 schematically illustrates aspects of management methods and arrangements according to embodiments of the present invention; and

(5) FIG. 4 schematically illustrates an optimization algorithm/function applied according to embodiments of the present invention.

DETAILED DESCRIPTION

(6) The illustration in the drawings is in schematic form. It is noted that in different figures, elements similar or identical in structure and/or function are provided with the same reference signs or with reference signs, which differ only within the first digit. A description of an element not described in one embodiment may be taken from a description of this element with respect to another embodiment.

(7) The hybrid power plant system 100 schematically illustrated in FIG. 1 comprises a hybrid power plant 110. The hybrid power plant 110 comprises at least one type of renewable power generation equipment 101a, 101b and comprises another type of renewable power generation equipment 103a, 103b. Furthermore, the hybrid power plant 110 comprises a storage system 105. The renewable power generation equipment 101a, 101b, 103a, 103b, 105 as well as the energy storage system 105 are all connected (for example via not illustrated breakers) to a point of common connection 107. Via a park transformer 109, the power is delivered to a utility system 111.

(8) Besides the hybrid power plant 110, the hybrid power plant system 100 comprises an arrangement 150 for power management according to an embodiment of the present invention. The power generation equipment 101, 103 and also the energy storage system 105 are controlled by a respective plant controller 113 and/or directly by a power delivery engine 115 which is part of the arrangement 150 for power management according to an embodiment of the present invention which is also denoted as energy management system. The arrangement 150 comprises at least one processor which is adapted to carry out or control a method of power management of the hybrid power plant 110.

(9) In the embodiment illustrated in FIG. 1, the arrangement 150 (energy management system EMS) is implemented in two stages or two blocks, namely a bid forecast calculation block or module 117 and the power delivery engine 115. The Bid forecast calculation block 117 is communicatively coupled to a forecast service 119 or an internal forecast module 119. From the module 119, the bid forecast calculation module 117 receives forecasts regarding energy production capability 121 and regarding energy price 123.

(10) In particular, the Bid forecast calculation block 117 uses (e.g. before a first dispatching time range 125) first forecasts of energy production capability and energy price to derive a first power generation schedule 127 defining power generation in the first dispatching time range 125. Further, at a dispatch point in time 129 which is within the first dispatching time range 125, the Bid forecast calculation module 117 uses a second forecast of energy production capability and energy price to derive a second power generation schedule 135 defining power generation in the second dispatching time range 133. Based on the first power generation schedule 127 and the second power generation schedule 135, the power delivery engine 115 calculates an optimal power injection schedule 137 for the first dispatch time range 125.

(11) The first schedule may e.g., be calculated by the Bid Forecast calculation algorithm, the second schedule may be calculated by the Engine, for the same time range. The Bid Foreacast may calculate a schedule that is later established as the reference for the Engine algorithm. The engine algorithm may take this schedule as an input and calculate the optimal power injection in order to dispatch the aforementioned schedule within tolerable bounds. This process example is repeated three times in the FIG. 1 and may be further continued repeatedly for later points in time.

(12) Later on, the process is continued by deriving a third power generation schedule 139 using third forecasts of energy production and capability, wherein the third power production schedule 139 relates to a third dispatching time range 141. In particular, the power delivery engine 115 may calculate the optimal power injection schedule 137 i.e., e.g. every 15 minutes for a respective 15 minute interval, 96 times a day.

(13) The incorporation of storage devices (for example 105 in FIG. 1) into renewable generation power plants together with an adequate power generation and energy price forecast has made it possible to develop predictive optimization algorithms oriented towards improving the energy management of the hybrid power plant.

(14) FIG. 2 schematically illustrates the arrangement for power management 250 in a schematic manner. The arrangement 250, also called energy management system, comprises two main sub-systems that allow the hybrid plant to be more predictable and dispatchable. The EMS may consider a hybrid plant as a unique and controllable power source.

(15) The energy management system EMS is based on the two stages, namely the Bid forecast calculation module 217 and the power delivery engine 215. The Bid forecast calculation module 217 calculates the future power generation schedule 227. The power delivery engine 215 optimizes the current available generation and the storage system capability or capacity at the moment of dispatch in order to guarantee the injection of the power into the grid as established by the power schedule 227 as determined by the Bid forecast calculation module 217, while avoiding power deviations that may be subject to penalizations.

(16) In particular, the Bid forecast calculation module 217 utilizes a natural forecast 221 (optionally iforecast data 249) as well as financial forecast 223 for calculating the power schedule 227. The power delivery engine 215 receives the bid reference (also referred to power schedule) 227 and finally calculates for each dispatch point in time an optimal power injection value 237 which is delivered to the hybrid power plant 210.

(17) The Bid forecast calculation may execute asynchronously or synchronously and the timescale is before the actual dispatching period. The power delivery engine 215 may execute synchronously and continuously and the timescale may be during the dispatching period. In this way, it may be possible for renewable energy plants which are provided with storage capability to present the characteristics of a traditional or dispatchable power plant, where it is possible to choose the optimal future power injection and being able to actually deliver it when the moment arrives.

(18) The target power schedule (e.g., 210 in FIG. 2) sent to the power plant may consider the production output at the point of interconnection (for example 107 in FIG. 1) or to the BESS (battery energy storage system) point of connection only or may also consider power or electrical characteristics at other points or nodes. Thereby, the energy management system does not control the storage system, wind turbine generators or photovoltaic inverters individually. Instead, a local plant controller (for example controller 113 in FIG. 1) and/or the power delivery engine 115 may have the power tracking possibility and it may be responsible for meeting the power reference by means of controlling the production of the individual generators and storage elements forming the power plant. The EMS will take all this into consideration and will send total power references only.

(19) FIG. 3 schematically illustrates which services may be performed or offered by the arrangement or the energy management system according to embodiments of the present invention. As a first service, capacity firming 343 is provided and as a second service, energy arbitrage/time-shifting 345 is provided. Based on a natural forecast 347 and using the power delivery engine 315, a deviation to previously scheduled power is mitigated providing deviation mitigation 340. Based on other forecast data 349 (iforecast) and using the power delivery engine 315, a technical optimization 362 is achieved.

(20) During energy arbitrage 345, a financial forecast algorithm 351 is used to economically optimize the power schedule, and the power delivery engine 315 calculates its optimal dispatch power schedule 370.

(21) In the following, exemplary embodiments of the two main modules, i.e., the Bid forecast calculation module and the power delivery engine are described. Embodiments of the present invention are not restricted to the following details.

(22) Bid Forecast Calculation:

(23) In the Bid Forecast Calculation stage of the EMS, the future power injection schedule is calculated and defined based on a predictive optimization algorithm. The optimization algorithm will take the electrical power production and market price forecasts for a certain time period in the future as an input, as well as the plants state and parameters, and it will calculate the optimal power injection for that period of time (optimal power injection schedule).

(24) This feature will allow the plant's participation in different energy markets (such as the day-ahead, intraday and continuous markets) where firm (e.g., constant with some margin) power value offers are mandatory.

(25) The optimization algorithm within the Bid Forecast Calculation is able to calculate the most adequate power generation and storage management strategy in order to achieve a specific power injection objective (market-based power time shifting and power injection optimization).

(26) The search of the optimal solution is bound to the feasible space defined by the necessary technical requirements included in the mathematical model formulation (constraints), such as the plant's maximum power injection capability, the battery's discharging and charging power and capacity, and its allowable charging levels.

(27) The optimization algorithm will calculate the optimal future power injection schedule based on the received forecasts and the estimated plant and battery's state for the considered planning time.

(28) The optimization algorithm was developed formulating the algorithm using continuous variables only instead of using a mixed-integer modelling. This allows to define a linear continuous model whose solution can be calculated using the SIMPLEX method (or its variation) without requiring specialized branch and bound and/or heuristic solution methods.

(29) Power Delivery Engine:

(30) In the Power Delivery Engine stage of the EMS, the optimal power values are calculated in order to allow the injection into the grid of the previously calculated optimal power injection schedule within the tolerable bounds.

(31) The optimization algorithm within the Power Delivery Engine will take the most recent power generation forecast as an input, as well as the plants state and parameters, and it will calculate the optimal power injection values considering that they must be maintained close to the previously calculated and defined schedule, avoiding deviations above the allowable percentage, thus avoiding penalizations.

(32) The search of the optimal solution is bound to the feasible space defined by the necessary technical requirements included in the mathematical model formulation (constraints), such as the allowable deviations percentage, the plant's maximum power injection capability, the battery's discharging and charging power and capacity, and its allowable charging levels.

(33) The main idea of the Power Delivery Engine, is a power reference tracking and deviations minimization algorithm (to avoid unnecessary battery operation) and the inclusion of a bound relaxation variable so the algorithm can converge to a solution even in scenarios of unexpectedly low energy availability at the moment of dispatch.

(34) The EMS may perform an optimization algorithm.

(35) FIG. 4 schematically illustrates in a schematic manner an optimization algorithm which may be performed by the Bid forecast calculation module and/or by the power delivery engine comprised in the arrangement for power management also denoted as EMS. As inputs, the optimization algorithm 460 receives for example the power limits of the hybrid plant, the power and capacity of the storage element or battery, the hybrid plant status and parameter, the power limits of the grid, the power generation forecasts and the grid energy forecasts as labelled with reference signs 461, 463. In an inside hardware and/or software module 465, an objective function 467 and plant's power and energy constraints 468 as well as grid's power constraints 469 may be definable or defined or considered. A solver 471 may solve to find an extreme of the objective function 467 subject to the constraints 468, 469 based on all (operational) input parameters 461, 463. The output may be the optimal power injection value (or schedule) 437 which may continuously be derived for subsequent dispatch points in time.

(36) A particular embodiment of the optimization algorithm is described below. Embodiments of the invention is not restricted to the optimization algorithm described below.

(37) Optimization Algorithms:

(38) The EMS is based on the modelling and resolution of mathematical optimization models. The model is formulated through one or more objective functions to optimize considering the feasible solution space defined by a set of constraints. These constraints are defined according to the necessary plant and grid's power and energy requirements.

(39) The optimization model is mathematically defined as follows:
Min{f.sub.1(x),f.sub.2(x), . . . ,f.sub.m(x)}
C.sub.k(x)0
D.sub.k(x)=0
x Where, f.sub.(x)=Objective function =Solution space C.sub.k=Inequality constraints D.sub.k=Equality constraints x=Modeled variables

(40) The optimization model is defined by an objective function and a set of constraints composed of continuous variables with a linear characteristic.

(41) Embodiments of the present invention may also allow to participate in an electricity market as will be described below:

(42) The EMS can e.g. publish a Production forecast for a specific time interval (this is done by the Bid Forecast Calculation Subsystem).

(43) If that forecast is confirmed (this could be done manually or automatically by an external system), The power delivery Engine will take the newest forecast and it will calculate an optimum production schedule considering the objective function and its constraints, that will be send to the local plant controller.

(44) The time span, intervals and update frequency can be adapted in order to adapt the system to different electricity markets or external control systems.

(45) As an advantage of embodiments of the present invention, an optimum production calendar for a certain timespan can be predicted and the local plant control may adjust the production to meet the reference.

(46) In order to overcome the lack of predictability and dispatchability of the renewable energy, virtual power plants (VPP) are being considered as a possible solution. The control of multiple generation resources may allow to compensate the generation errors between them. However, virtual power plants may be a solution at a distribution and transmission system level but are not being considered at power plant point of common connection level unlike the proposed energy management system.

(47) Although the present invention has been disclosed in the form of preferred embodiments and variations thereon, it will be understood that numerous additional modifications and variations could be made thereto without departing from the scope of the invention.

(48) For the sake of clarity, it is to be understood that the use of a or an throughout this application does not exclude a plurality, and comprising does not exclude other steps or elements.