DESC:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
METHOD AND SYSTEM TO OPTIMIZE HETEROGENEOUS STORAGE RESOURCES ACROSS VARIOUS ELECTRIC POWER SYSTEM NETWORK

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
The present application claims priority from Indian provisional patent application number 202321052651, filed on August 04, 2023. The entire content of the abovementioned application is incorporated herein by reference.
TECHNICAL FIELD
The disclosure herein generally relates to the field of energy storage system (ESS) optimization, and, more particularly, to a method and system for an ESS aggregator to optimize large and heterogeneous storage resources across various electric power system network.
BACKGROUND
The System Operator (SO) has the responsibility of managing the demand-supply balance in the electric power system network in real-time. Growing proportion of intermittent renewable energy sources (RES) in the power system has been a concern for SOs world-wide. Not only is the power generated by these sources intermittent due to their dependency on weather, but the rapid rate of change of power output makes it difficult for the SO to manage the demand-supply balance with only conventional balancing mechanism units (BMU) which are typically rotating synchronous generators. Though the RES help in achieving the sustainability goals of decarbonization of the power system, there are new challenges in forecasting the generation and managing rapidly changing demand-supply imbalances in real-time. The low/no inertia of the RES necessitates flexible mechanisms/services to help the SO.
The SO must always ensure a balance between supply and demand in real time, while considering the consumption and generation variability. To maintain this balance, the SO requires dispatchable power reserves of certain capacity in both directions - upward and downward, within its geographical area. These reserves are a reactive means to level out the frequency deviations in the power grid and can be traded through ancillary service markets/balancing markets. Balancing markets include capacity markets and energy markets. Balancing energy is the energy utilized by the SO to manage the frequency deviations and maintain demand supply balance. Balancing capacity is the flexible capacity which is made available or kept on standby by balancing service providers (BSPs) for a certain duration in order to provide balancing energy whenever required.
SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method to optimize large and heterogeneous storage resources across various electric power system network is provided. The processor-implemented method includes receiving, via an input/output interface, a historical market data of one or more ancillary services by an Energy Storage System (ESS) aggregator in an ancillary service market and forecasting, via one or more hardware processors, an ancillary price for each of the one or more ancillary services using a standard univariate Auto Regressive Integrated Moving Average Method (ARIMA) or any other appropriate forecasting model based on the received historical market data of one or more ancillary services.
Further, the processor-implemented method comprises computing, via the one or more hardware processors, an amortized capacity cost for a predefined time slot for bidding in the capacity market using information of one or more heterogeneous storage resources, selecting, via the one or more hardware processors, one or more decision variables to satisfy one or more constraints for bidding a volume for each of the one or more service at the ancillary market and estimating, via the one or more hardware processors, a probability of activation of each time slot of aggregators’ reserved capacity for each of the one or more ancillary services by using a lower and an upper limits of forecasted activation price.
Furthermore, the processor-implemented method comprises approximating, via one or more hardware processors, a Non-Linear Programming (NLP) problem by replacing each of the one or more binary variables with a continuous variable within bounds of [0, 1] and by adding one or more appropriate penalties in an objective function of the probability of activation, and approximating, via the one or more hardware processors, the output of the NLP problem by fixing values of a Mixed-Integer Linear Programming (MILP) problem. Finally, the processor-implemented method comprises optimizing, via the one or more hardware processors, participation of the heterogeneous storage resources across each of the one or more ancillary services.
In another aspect, a system for an ESS aggregator to optimize large and heterogeneous storage resources across various electric power system network is provided. The system comprises a memory storing a plurality of instructions and one or more Input/Output (I/O) interfaces to receive a historical market data of one or more ancillary services by an Energy Storage System (ESS) aggregator in an ancillary service market. Further, the system comprises one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to forecast an ancillary price for each of the one or more ancillary services using a standard univariate Auto Regressive Integrated Moving Average Method (ARIMA) or any other appropriate forecasting model based on the received historical market data of one or more ancillary services, wherein the ancillary price includes an activation price and a capacity price. Further, the one or more hardware processors are configured to compute an amortized capacity cost for a predefined time slot for bidding in the capacity market using information of one or more heterogeneous storage resources. Furthermore, the one or more hardware processors are configured to select one or more decision variables to satisfy one or more constraints for bidding a volume for each of the one or more service at the ancillary market.
Further, the one or more hardware processors are configured to estimate a probability of activation of each time slot of aggregators’ reserved capacity for each of the one or more ancillary services by using a lower and an upper limits of forecasted activation price. Further, the one or more hardware processors are configured to approximate a Non-Linear Programming (NLP) problem by replacing each of the one or more binary variables with a continuous variable within bounds of [0, 1] and by adding one or more appropriate penalties in an objective function of the probability of activation. Furthermore, the one or more hardware processors are configured to approximate the output of the NLP problem by fixing values of a Mixed-Integer Linear Programming (MILP) problem. Finally, the one or more hardware processors are configured to optimize participation of the heterogeneous storage resources across each of the one or more ancillary services.
In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions, which when executed by one or more hardware processors causes a method to optimize large and heterogeneous storage resources across various electric power system network is provided. The processor-implemented method includes receiving, via an input/output interface, a historical market data of one or more ancillary services by an Energy Storage System (ESS) aggregator in an ancillary service market and forecasting, via one or more hardware processors, an ancillary price for each of the one or more ancillary services using a standard univariate Auto Regressive Integrated Moving Average Method (ARIMA) based on the received historical market data of one or more ancillary services.
Further, the processor-implemented method comprises computing, via the one or more hardware processors, an amortized capacity cost for a predefined time slot for bidding in the capacity market using information of one or more heterogeneous storage resources, selecting, via the one or more hardware processors, one or more decision variables to satisfy one or more constraints for bidding a volume for each of the one or more service at the ancillary market and estimating, via the one or more hardware processors, a probability of activation of each time slot of aggregators’ reserved capacity for each of the one or more ancillary services by using a lower and an upper limits of forecasted activation price.
Furthermore, the processor-implemented method comprises approximating, via one or more hardware processors, a Non-Linear Programming (NLP) problem by replacing each of the one or more binary variables with a continuous variable within bounds of [0, 1] and by adding one or more appropriate penalties in an objective function of the probability of activation, and approximating, via the one or more hardware processors, the output of the NLP problem by fixing values of a Mixed-Integer Linear Programming (MILP) problem. Finally, the processor-implemented method comprises optimizing, via the one or more hardware processors, participation of the heterogeneous storage resources across each of the one or more ancillary services.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
FIG. 1 illustrates an exemplary system to optimize large and heterogeneous storage resources across various electric power system network, according to some embodiments of the present disclosure.
FIG. 2 is a functional block diagram to illustrate an optimization framework for ESS aggregator to bid in balancing market, according to some embodiments of the present disclosure.
FIG. 3 is a functional block diagram to illustrate balancing market timeline, according to some embodiments of the present disclosure.
FIG. 4A and 4B is an exemplary flow diagram illustrating a processor-implemented method to optimize large and heterogeneous storage resources across various services, according to some embodiments of the present disclosure.
FIG. 5A and 5B are schematic diagrams to illustrate capacity and activation prices of different services for a sample day, according to some embodiments of the present disclosure.
FIG. 6 is a schematic diagram to illustrate forecasted balancing market prices for a sample day, according to some embodiments of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS
Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
With increasing proportion of intermittent renewable energy sources in the electric power system, the system operator (SO) is facing a challenge of maintaining the demand-supply balance which is more dynamic and uncertain than before. To manage the balance, the SO procures different services from balancing service providers like balancing mechanism units and distributed energy resources aggregators.
An Energy storage system (ESS) is an ideal resource to provide balancing services due to its high flexibility. It can provide these services in both directions, upward direction by discharging and downward direction by charging. Also, it has quick response time, i.e., it can instantaneously start charging or discharging. If multiple ESS are aggregated together to get some specified capacity and are operated appropriately, they can participate in balancing markets and provide services while earning revenue.
Since each service has different specifications, the proposed formulation captures the regulations and bidding characteristics of all the four services individually. The proposed formulation is a Mixed Integer Linear Programming (MILP) problem, problem of optimizing a battery storage’s simultaneous participation in all the frequency regulation services has not been discussed in the state of the art.
There is a continuous rise in the number of small size batteries (standalone or solar-ESS combination) being installed across the distribution network by the residential users. An aggregator can aggregate them and use them for trading in markets. However, large number of ESS with the aggregator will lead to increase in binary variables in the original MILP formulation which will increase its computational complexity. Hence, an alternate approximation method is proposed which is scalable and takes significantly less computation time while giving comparable results.
State of the arts consider a constant probability of activation (real time deployment ratios) in balancing energy markets for the entire day in their models. However, it varies over time slots and is different for different services as it depends on values of real time system imbalances and the duration of each event.
Embodiments herein provide a method and system to optimize large and heterogeneous storage resources across various electric power system network. The System Operator (SO) has the responsibility of managing the demand-supply balance in the electric power system network in real-time. To maintain this balance, the SO requires dispatchable power reserves of certain capacity in both directions - upward and downward, within its geographical area. These reserves are a reactive means to level out the frequency deviations in the power grid and can be traded through ancillary service markets/balancing markets. Balancing markets include capacity markets and energy markets. Balancing energy is the energy utilized by the SO to manage the frequency deviations and maintain demand supply balance. Balancing capacity is the flexible capacity which is made available or kept on standby by balancing service providers (BSPs) for a certain duration in order to provide balancing energy whenever required.
The distributed small size ESS can be grouped and utilized by the aggregator for trading of multiple services with different specifications and bidding rules in ancillary service markets. ESS, being flexible and having quick response time, can contribute in both directions for all the services and assist in maintaining the real time demand-supply balance of the system.
Further, the disclosure proposes a trading strategy across all services of electric power system network in a balancing market as an operation research problem. This trading strategy enables an ESS aggregator to bid for multiple services simultaneously while adhering to each service’s bidding rules. an approximation method is put forward which is capable of achieving computational scalability without significantly sacrificing on the bidding performance. The method herein is tested using real world data traces. It performs better than the baseline method. Our approximation reduces the computation time with respect to the original MILP method by as much as 50% while giving comparable optimality. The disclosed strategy can be further extended to include different distributed energy resources (DERs) like roof-top solar photovoltaics (PV), Electric vehicle (EV), demand response in the aggregator’s portfolio.
Referring now to the drawings, and more particularly to FIG. 1 through FIG. 6, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 illustrates a block diagram of a system 100 to optimize large and heterogeneous storage resources across various services, in accordance with an example embodiment. Although the present disclosure is explained considering that the system 100 is implemented on a server, it may be understood that the system 100 may comprise one or more computing devices 102, such as a laptop computer, a desktop computer, a notebook, a workstation, a cloud-based computing environment and the like. It will be understood that the system 100 may be accessed through one or more input/output interfaces 104-1, 104-2... 104-N, collectively referred to as I/O interface 104. Examples of the I/O interface 104 may include, but are not limited to, a user interface, a portable computer, a personal digital assistant, a handheld device, a smartphone, a tablet computer, a workstation, and the like. The I/O interface 104 are communicatively coupled to the system 100 through a network 106.
In an embodiment, the network 106 may be a wireless or a wired network, or a combination thereof. In an example, the network 106 can be implemented as a computer network, as one of the different types of networks, such as virtual private network (VPN), intranet, local area network (LAN), wide area network (WAN), the internet, and such. The network 106 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), and Wireless Application Protocol (WAP), to communicate with each other. Further, the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices. The network devices within the network 106 may interact with the system 100 through communication links.
The system 100 supports various connectivity options such as BLUETOOTH®, USB, ZigBee, and other cellular services. The network environment enables connection of various components of the system 100 using any communication link including Internet, WAN, MAN, and so on. In an exemplary embodiment, the system 100 is implemented to operate as a stand-alone device. In another embodiment, the system 100 may be implemented to work as a loosely coupled device to a smart computing environment. Further, the system 100 comprises at least one memory 110 with a plurality of instructions, one or more databases 112, and one or more hardware processors 108 which are communicatively coupled with the at least one memory to execute a plurality of modules 114 therein. The plurality of modules 114, for example, includes a market price forecasting module 202, an activation module 204, and an optimization module 206. The components and functionalities of the system 100 are described further in detail.
FIG. 2 is a functional block diagram 200 illustrating an optimization framework for ESS aggregator to bid in balancing market implemented by the system 100 of FIG. 1 through the exemplary block diagram 300 of FIG. 3. The objective of the ESS aggregator is maximize its profit by optimally trading for all the ancillary services by utilizing its available capacity as indicated in equation 1.
max-(V,P^chg,P^dsg )??_(t?T)¦(R_t^FCR+R_t^aFRR+R_t^mFRR+R_t^RR ) -?_(b?B)¦C_b^"deg" (1)
wherein, V=\{fcr^Up,fcr^Dn,afrr^Up,afrr^Dn,mfrr^Up,mfrr^Dn,rr^Up,rr^Dn \} is the set of decision variables corresponding to bid volumes for all the services in both the direction, T is the set of hours of the data over the optimization is run Pcharging and Pdischarging is the set of all the charging and discharging volumes for batteries
b?B and hours t?T.
In many major electricity markets, distributed energy resources (DERs) aggregators participating as a balancing service providers (BSPs) faces the problem of optimizing its resources with complex inter-temporal constraints of various services. A typical balancing market timeline is shown in FIG. 3. The BSP who wishes to provide a particular balancing service for a day, can first bid for balancing capacity either through annual tenders or daily tenders. When bidding for day D, a BSP can submit its bids in capacity markets for the entire day on D - 1 by gate closure of 11 a.m. Market clearing for balancing capacity is merit order based with the objective of procurement cost minimization. This clearing is done immediately, and the result of clearing is relayed back to the BSPs on D - 1 by 12 p.m. If the bid is cleared and procured by the SO, it has to keep the procured capacity available (on standby) for the entire cleared duration and bid the entire volume in balancing energy markets. The bids for a slot T of the actual delivery day D in balancing energy markets can be updated till the previous time slot T - 1.
Activation of the procured capacity in energy markets is subject to real time system imbalances. There are two streams of revenues from these markets:
1) capacity payment for being available, and
2) activation payment for actual deployment of balancing energy.
The balancing markets have defined services or products based on different activation methods, activation speeds and activation response timescales for which a BSP can bid for. Some of standard balancing services present in the major electricity markets are:
Frequency Containment Reserve (FCR)
automatic Frequency Restoration Reserve (aFRR)
manual Frequency Restoration Reserve (mFRR)
Replacement Reserve (RR)
FIG. 4A and 4B (collectively referred as FIG. 4) is a flow diagram illustrating a processor-implemented method 400 for to optimize large and heterogeneous storage resources across various services implemented by the system 100 of FIG. 1. Functions of the components of the system 100 are now explained with reference to FIG. 2 through steps of flow diagram in FIG. 4, according to some embodiments of the present disclosure.
Initially, at step 402 of the processor-implemented method 400, the one or more hardware processors 108 are configured by the programmed instructions to receive, via an input/output interface, a historical market data of one or more ancillary services by an Energy Storage System (ESS) aggregator in an ancillary service market. The one or more ancillary services include frequency containment reserve (FCR), automatic Frequency Restoration Reserve (aFRR), manual Frequency Restoration Reserve (mFRR), and Replacement Reserve (RR).
At the next step 404 of the processor-implemented method 400, the one or more hardware processors 108 are configured by the programmed instructions to forecast an ancillary price for each of the one or more ancillary services using a standard univariate Auto Regressive Integrated Moving Average Method (ARIMA) based on the received historical market data of one or more ancillary services. The ancillary price includes an activation price and a capacity price.
The volume bid for the services at time t is provided by appropriately charging (p_(t,b)^chg) and discharging (p_(t,b)^dsg) of the batteries. Continuous utilization of a battery affects its health which decreases its life. The depreciation of a battery’s health is quantified through degradation cost as calculated in the equation (2).
C_b^"deg" =? (C_b^"inv" )/?"Cap" ?_b ?_(t=1)^T¦?(p_(t,b)^chg+p_(t,b)^dsg ) ?_t ?,?b?B (2)
wherein, ? is the linear approximation of the slope of the degradation curve with respect to time, in terms of number of lifecycles and hours of operation. C_b^"inv" is the investment of battery b while ?"Cap" ?_b is its capacity in kWh. Thus, the term ? (C_b^"inv" )/?"Cap" ?_b is equivalent to the degradation cost per unit energy. Including degradation cost in the objective function ensures that no battery is over exploited, i.e., all the batteries are utilized appropriately based on their capital cost and capacities.
For FCR, the entire day is divided in six blocks where each block constitutes of four hours. The set of blocks is defined as TFCR = {k1, k2, k3, . . . , k6}. Each k is a set of four hours such that k1 denotes {t = 1 . . . 4}, k2 denotes {t = 5 . . . 8}, k3 denotes {t = 9 . . . 12}, . . . , k6 denotes {t = 21 . . . 24}. It indicates that the bid volume in each slot t in any block k should be equal which is as:
fcr_(t_1)^Up=fcr_(t_2)^Up,?t_1,t_2?k,?k?T^FCR (3)
wherein FCR product bidding has to be symmetric, fcrUp volume should be equal to fcrDn volume at any given time slot t as:
fcr_t^Up=fcr_t^Dn,?t?T (4)
The contract interval for aFRR is one hour. The aggregator can independently bid for one or more consecutive hours. Thus, for aFRR, one block constitutes of one hour i.e., T aFRR = { k1, k2, k3, . . . , k24} where k1 denotes {t = 1}, k2 denotes {t = 2}, k3 denotes {t = 3}, . . . , k24 denotes {t = 24}. Its bidding may not be symmetric in both directions.
Furthermore, for the mFRR and RR constraints, SO requires a bid for an entire day commitment. Thus, for these products, the entire day is considered as a single block such that T manual = { k1} where k1 denotes {1, 2, 3, . . . , 24}. Bid volume for all the time slots t for the entire day has to be the same:
mfrr_(t_1)^Up=mfrr_(t_2)^Up,?t_1,t_2?k,?k?T^manual (5)
mfrr_(t_1)^Dn=mfrr_(t_2)^Dn,?t_1,t_2?k,?k?T^manual (6)
rr_(t_1)^Up=rr_(t_2)^Up,?t_1,t_2?k,?k?T^manual (7)
rr_(t_1)^Dn=rr_(t_2)^Dn,?t_1,t_2?k,?k?T^manual (8)
At the next step 406 of the processor-implemented method 400, the one or more hardware processors 108 are configured by the programmed instructions to compute an amortized capacity cost for a predefined time slot for bidding in the capacity market using information of one or more heterogeneous storage resources. The information of one or more heterogeneous storage resources comprises of a battery capacity, an investment cost, an availability, maximum charge/discharge rate and State of Charge (SoC) limit.
The volume bid in all the services at time t is due to charging and discharging of the batteries. Providing down services is equivalent to charging the batteries while up services results in discharging of batteries respectively.
?_(b?B)¦p_(t,b)^chg =fcr_t^Dn+afrr_t^Dn+mfrr_t^Dn+rr_t^Dn,?t?T (9)
?_(b?B)¦p_(t,b)^dsg =fcr_t^Up+afrr_t^Up+mfrr_t^Up+rr_t^Up,?t?T (10)
At any given time slot t, the aggregator can bid for both upward and downward services. However, one battery can contribute to only one direction of service (either up or down). Thus, for a time slot t, if the aggregator is bidding for both directions, one subset of batteries is scheduled for up direction while another exclusive subset gets scheduled for down direction. There is no overlapping of batteries between these two subsets. This enables the aggregator to bid for both directions without any energy arbitrage happening between its own assets.
At the next step 408 of the processor-implemented method 400, the one or more hardware processors 108 are configured by the programmed instructions to select one or more decision variables to satisfy one or more constraints for bidding a volume for each of the one or more service at the ancillary market. Wherein the one or more constraints include a regulation and one or more bidding characteristics of each of the one or more ancillary services.
The optimal bidding schedule is dependent on the market prices. As the market prices vary on a daily basis and the aggregator needs to create the bidding schedule on a day ahead basis, it needs to use the forecasted prices as actual prices are not known. Price forecasting in the proposed approach is done using the standard univariate Auto Regressive Integrated Moving Average (ARIMA) method. The bids that get cleared and are called for deployment are paid with the marginal clearing price (forecasted activation market price in our case). Thus, the probability of activation (p) of the aggregator’s reserved capacity will depend on the relative values of bid amortized cost and the forecasted market price.
At the next step 410 of the processor-implemented method 400, the one or more hardware processors 108 are configured by the programmed instructions to estimate a probability of activation of each time slot of aggregators’ reserved capacity for each of the one or more ancillary services by using a lower and an upper limits of forecasted activation price, wherein the probability of activation follows a linear function and takes a corresponding value below 0 to 1.
FIG. 5A and 5B are schematic diagrams to illustrate capacity and activation prices of different services for a sample day, according to some embodiments of the present disclosure. Based on the average price of the sample day (considering both activation and capacity prices), the highest paying service is mfrr^Up followed by fcr^Dn. Thus, for the baseline method, the aggregator schedules its capacity in these two services. The aggregator bids most of its capacity in mfrr^Up followed by less volumes in fcr^Up and fcr^Dn services. As fcr is a symmetric service, the aggregator bids equal volumes in both directions whereas a full day commitment is seen in mfrr^Up which complies with the bidding rules.
At the next step 412 of the processor-implemented method 400, the one or more hardware processors 108 are configured by the programmed instructions to approximate a Non-Linear Programming (NLP) problem by replacing each of the one or more binary variables with a continuous variable within bounds of [0, 1] and by adding one or more appropriate penalties in an objective function of the probability of activation. These penalty terms are such that the variables are pushed to take values either close to 0 or 1 which indirectly mimics the function of a binary variable. Further, the output of the NLP problem is approximated by fixing values of a Mixed-Integer Linear Programming (MILP) problem.
Out of the two disclosed techniques, original MILP performs better both in terms of profit and scheduled volumes. However, the computation time taken by these methods is different as shown in FIG. 6. It can be seen that the total time taken by the approximation method is considerably less than the time taken by the original MILP. Computation time of approximation method is almost 53% less than that of the original MILP method. This is because - in original MILP all the a’s is considered as binary variables which makes the problem tedious resulting in a longer computation time. For the proposed approximation method, the first step of approximated NLP gets solved quickly as all the variables are continuous in nature. For the second step of reduced MILP, 56.8% of the a’s close to 0 or 1 are approximated and passed as parameters and only 43.2% of a’s is passed as binary variables. This decrease in the number of binary variables leads to reduction of total computation time of the method. It is to be noted that the results obtained from original MILP are with a mip gap setting of 3%. It can be said that there is a considerable saving in computation time with the approximation method when compared with original MILP with the mip gap setting of 3%. The average time taken by original MILP to solve one day is around 1988 seconds (33 minutes) while the time taken by the approximation method is 655 seconds (11 minutes) respectively.
Finally, at the last step 414 of the processor-implemented method 400, the one or more hardware processors 108 are configured by the programmed instructions to optimize participation of the heterogeneous storage resources across each of the one or more ancillary services.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
The embodiments of present disclosure herein address unresolved problem of optimizing among multiple ancillary services with different characteristics e.g., activation time, minimum volume etc. with the constraints on time slots for providing these services for an aggregator with large number of energy storage systems. Embodiments herein provide a model for an aggregator of energy storage systems (ESS) is disclosed. The distributed small size ESS can be grouped and utilized by the aggregator for trading of multiple services with different specifications and bidding rules in ancillary service markets. ESS, being flexible and having quick response time, can contribute in both directions for all the services and assist in maintaining the real time demand-supply balance of the system.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means, and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.
,CLAIMS:
1. A processor-implemented method (400), comprising:
receiving (402), via an input/output interface, a historical market data of one or more ancillary services by an Energy Storage System (ESS) aggregator in an ancillary service market;
forecasting (404), via one or more hardware processors, an ancillary price for each of the one or more ancillary services using a standard univariate Auto Regressive Integrated Moving Average Method (ARIMA) based on the received historical market data of one or more ancillary services, wherein the ancillary price includes an activation price and a capacity price;
computing (406), via the one or more hardware processors, an amortized capacity cost for a predefined time slot for bidding in the capacity market using information of one or more heterogeneous storage resources;
selecting (408), via the one or more hardware processors, one or more decision variables to satisfy one or more constraints for bidding a volume for each of the one or more ancillary service at the ancillary market;
estimating (410), via the one or more hardware processors, a probability of activation of each time slot of aggregators reserved capacity for each of the one or more ancillary services by using a lower and an upper limits of forecasted activation price;
approximating (412), via one or more hardware processors, a Non-Linear Programming (NLP) problem by replacing each of one or more binary variables with a continuous variable within bounds of [0, 1] and by adding one or more appropriate penalties in an objective function of the probability of activation;
approximating (414), via the one or more hardware processors, the output of the NLP problem by fixing values of a Mixed-Integer Linear Programming (MILP) problem; and
optimizing (416), via the one or more hardware processors, participation of the heterogeneous storage resources across each of the one or more ancillary services.

2. The processor-implemented method (400) as claimed in claim 1, wherein if the amortized capacity cost is less than or equal to lower limit of the computed activation price, then entire capacity volume is called upon for a deployment in real time.

3. The processor-implemented method (400) as claimed in claim 1, wherein one or more ancillary services include a frequency containment reserve (FCR), an automatic Frequency Restoration Reserve (aFRR), a manual Frequency Restoration Reserve (mFRR), and a Replacement Reserve (RR).

4. The processor-implemented method (400) as claimed in claim 1, wherein the information of one or more heterogeneous storage resources comprises of a battery capacity, an investment cost, an availability, a maximum charge/discharge rate and a State of Charge (SoC) limit.

5. The processor-implemented method (400) as claimed in claim 1, wherein the one or more constraints include a regulation and one or more bidding characteristics of each of the one or more ancillary services.

6. The processor-implemented method (400) as claimed in claim 1, wherein the probability of activation follows a linear function and takes a corresponding value below 0 to 1.

7. A system (100) comprising:
one or more input/output interfaces (104) to receive a historical market data of one or more ancillary services by an Energy Storage System (ESS) aggregator in an ancillary service market;
a memory (110) storing instructions; and
one or more hardware processors (108) coupled to the memory (110) via the one or more input/output interfaces (104), wherein the one or more hardware processors (108) are configured by the instructions to:
forecast an ancillary price for each of the one or more ancillary services using a standard univariate Auto Regressive Integrated Moving Average Method (ARIMA) based on the received historical market data of one or more ancillary services, wherein the ancillary price includes an activation price and a capacity price;
compute an amortized capacity cost for a predefined time slot for bidding in the capacity market using information of one or more heterogeneous storage resources;
select one or more decision variables to satisfy one or more constraints for bidding a volume for each of the one or more ancillary service at the ancillary market;
estimate a probability of activation of each time slot of aggregators’ reserved capacity for each of the one or more ancillary services by using a lower and an upper limits of forecasted activation price;
approximate a Non-Linear Programming (NLP) problem by replacing each of one or more binary variables with a continuous variable within bounds of [0, 1] and by adding one or more appropriate penalties in an objective function of the probability of activation;
approximate the output of the NLP problem by fixing values of a Mixed-Integer Linear Programming (MILP) problem; and
optimize participation of the heterogeneous storage resources across each of the one or more ancillary services.

8. The system (100) as claimed in claim 7, wherein if the amortized capacity cost is less than or equal to lower limit of the computed activation price, then entire capacity volume is called upon for a deployment in real time.

9. The system (100) as claimed in claim 7, wherein one or more ancillary services include a frequency containment reserve (FCR), an automatic Frequency Restoration Reserve (aFRR), a manual Frequency Restoration Reserve (mFRR), and a Replacement Reserve (RR).

10. The system (100) as claimed in claim 7, wherein the information of one or more heterogeneous storage resources comprises of a battery capacity, an investment cost, an availability, maximum charge/discharge rate and a State of Charge (SoC) limit.

11. The system (100) as claimed in claim 7, wherein the one or more constraints include a regulation and one or more bidding characteristics of each of the one or more ancillary services.

12. The system (100) as claimed in claim 7, wherein the probability of activation follows a linear function and takes a corresponding value below 0 to 1.