The subject matter described herein relates generally to power distribution systems and, more particularly, to load (demand) side power grid control.
The power generation industry is transitioning from being primarily based on a small number of large centralized power plants to a diversified network that combines conventional power plants, renewable power generation (e.g., solar, wind and the like), energy storage and microgrids. Traditionally, power grids have been designed to accommodate variable load demand, in which central-station power plants at a transmission level provide services down to the industrial, commercial, and residential end users at a distribution level.
As renewable energy is becoming a major contributor in meeting demand, grid control systems need to provide more flexibility for power systems to compensate for the volatility of renewable energy. More particularly, due to uncertainties in renewable energy resource availability, renewable power generation cannot be accurately forecast.
To facilitate the penetration of renewable power generation (such as wind turbines and solar panels) into the power grid, load-side control is used. In general, the purpose of load-side control is to attempt to optimize the collective power consumption of the loads (such as buildings), including to accommodate uncertainties in the renewable power generation.
In one aspect, a method for controlling a distributed power system is provided, the power system including an aggregator communicatively coupled to a plurality of nodes, each of the plurality of nodes including an associated load. The method includes receiving, at the aggregator, a specified aggregated power level from an independent service operator, and at each of a plurality of sample times recurring at a regular interval, receiving, at the aggregator from each of the plurality of nodes, a condensed dataset including load-specific information for that node, calculating, at the aggregator, a global value based on the specified aggregated power level, the plurality of condensed datasets, and a control prediction horizon, wherein the control prediction horizon is a time period that is an integer multiple of the regular interval, transmitting the global value to each of the plurality of nodes, solving, at each of the plurality of nodes, a local optimization problem for that node based on the received global value and a local model prediction horizon for that node, wherein the local model prediction horizon for at least one node of the plurality of nodes is longer than the control prediction horizon, and controlling, at each of the plurality of nodes, the load based on the solved local optimization problem for that node.
In another aspect, a distributed power system is provided. The distributed power system includes a plurality of nodes, each node including an associated load, and an aggregator communicatively coupled to the plurality of nodes, the aggregator configured to receive a specified aggregated power level from an independent service operator, and at each of a plurality of sample times recurring at a regular interval, receive from each of the plurality of nodes, a condensed dataset including load-specific information for that node, calculate a global value based on the specified aggregated power level, the plurality of condensed datasets, and a control prediction horizon, wherein the control prediction horizon is a time period that is an integer multiple of the regular interval, and transmit the global value to each of the plurality of nodes. Each of the plurality of nodes is configured to, at each of the plurality of sample times, solve a local optimization problem for that node based on the received global value and a local model prediction horizon for that node, wherein the local model prediction horizon for at least one node of the plurality of nodes is longer than the control prediction horizon, and control the load based on the solved local optimization problem for that node.
In yet another aspect, a node for use in controlling a distributed power system is provided. The node has an associated load and is communicatively coupled to an aggregator. The node includes a memory device, and a processor communicatively coupled to the memory device, the processor configured to, at each of a plurality of sample times recurring at a regular interval, receive, from the aggregator, a global value, wherein the global value is calculated by the aggregator based on a specified aggregated power level received from an independent service operation, a condensed dataset including load-specific information for the node, and a control prediction horizon, wherein the control prediction horizon is a time period that is an integer multiple of the regular interval, solve a local optimization problem based on the received global value and a local model prediction horizon for the node, wherein the local model prediction horizon for the node of the plurality of nodes is longer than the control prediction horizon, and control the load based on the solved local optimization problem for the node.
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Unless otherwise indicated, the drawings provided herein are meant to illustrate features of embodiments of the disclosure. These features are believed to be applicable in a wide variety of systems comprising one or more embodiments of the disclosure. As such, the drawings are not meant to include all conventional features known by those of ordinary skill in the art to be required for the practice of the embodiments disclosed herein.
In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings.
The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
Approximating language, as used herein throughout the specification and claims, may be applied to modify any quantitative representation that could permissibly vary without resulting in a change in the basic function to which it is related. Accordingly, a value modified by a term or terms, such as “about,” “substantially,” and “approximately,” are not to be limited to the precise value specified. In at least some instances, the approximating language may correspond to the precision of an instrument for measuring the value. Here and throughout the specification and claims, range limitations may be combined and/or interchanged, such ranges are identified and include all the sub-ranges contained therein unless context or language indicates otherwise.
The systems and methods described herein enable controlling a distributed power system that includes an aggregator communicatively coupled to a plurality of nodes, each of the plurality of nodes including an associated load. The aggregator receives a specified aggregated power level from an independent service operator. At each of a plurality of sample times recurring at a regular interval, the aggregator receives, from each of the plurality of nodes, a condensed dataset including load-specific information for that node. The aggregator calculates a global value based on the specified aggregated power level, the plurality of condensed datasets, and a control prediction horizon, wherein the control prediction horizon is a time period that is an integer multiple of the regular interval, and transmits the global value to each of the plurality of nodes. Each of the plurality of nodes solves a local optimization problem for that node based on the received global value and a local model prediction horizon for that node, wherein the local model prediction horizon for at least one node of the plurality of nodes is longer than the control prediction horizon, and controls the load based on the solved local optimization problem for that node.
Various aspects of the technology described herein are generally directed towards distributed load-side power control. In the embodiments described herein, the technology includes a distributed optimization approach for control of an aggregation of distributed flexibility resources (DFRs, which may be referred to as nodes), such that a commanded power profile (e.g., specified by an independent service operator) is produced by aggregated loads. In general, and as will be understood, the technology is based on a distributed iterative solution to solve a network utility maximization problem. Distributed load-side power control systems are described in detail in U.S. patent application Ser. No. 15/462,136, filed Mar. 17, 2017, and International Application No. PCT/US2018/050903, filed Sep. 13, 2018, both of which are incorporated by reference herein in their entirety.
In general, each distributed flexibility resource node solves a local optimization problem with its own local constraints (e.g., limits on temperatures on zones within a building) and states. However, the cost function incorporates a global (that is, shared by all nodes) Lagrange multiplier to augment information from the full set of distributed nodes, to track the need for additional power (via a reserve request signal) as an aggregate entity. To this end, the global Lagrange multiplier is calculated at an aggregation level using information that is gathered from each node, with the newly calculated global Lagrange multiplier, associated with the constraint that requests that aggregated power needs to equal the command power generated by an independent service operator, broadcast to each node. The operations are iterative; each node uses the received Lagrange multiplier to perform one iteration of an optimization algorithm that calculates the search direction using the given Lagrange multiplier and sends updated information to an aggregator, which then recalculates a new Lagrange multiplier from the updated information, broadcasts the new Lagrange multiplier to the nodes for a next optimization iteration, and so on, until the aggregator determines that a sufficient level (some defined level) of convergence is reached. This procedure is performed at each sample time and once convergence occurs, loads apply the computed optimal control.
It should be understood that any of the examples herein are non-limiting. As such, the technology described herein is not limited to any particular implementations, embodiments, aspects, concepts, structures, functionalities or examples described herein. Rather, any of the implementations, embodiments, aspects, concepts, structures, functionalities or examples described herein are non-limiting, and the technology may be used in various ways that provide benefits and advantages in power control concepts in general.
In general and as represented in
Represented nodes 102(1)-102(N), include controllers 104(1)-104(N) having model predictive control algorithms 106(1)-106(N) and information matrixes 108(1)-108(N) that describe the local states and constraints of the node. One example state is a local weather forecast obtained from a source 114 such as the National Oceanic and Atmospheric Administration (NOAA), as the weather influences renewable power source output as well as corresponds to how much power a node is likely to need for heating or cooling, for example. Other state that may be maintained may be room temperatures, zone temperatures, heating and cooling characteristics, time of day, day of week and so on.
Nodes 102(1)-102(N) are also shown in
Nodes 102(1)-102(N) are coupled via their respective controllers 104(1)-104(N) to an aggregator 116. As will be appreciated by those of skill in the art, aggregator 116 may be implemented as a computing device that includes a processor communicatively coupled to a memory device. In general and as described herein, aggregator 116 receives a commanded power profile from an independent service operator (ISO) 118, and uses that information along with local load-specific information received from each controller 104(1)-104(N) to compute a global Lagrange multiplier 120 mathematically represented by ρk (at iteration k). Global Lagrange multiplier 120 computed by aggregator 116 is sent to controllers 104(1)-104(N), for use by each controller in solving its local optimization problem.
To summarize thus far, the technology is directed towards attempting to more optimally control distributed flexibility resource nodes, such that the aggregated power tracks a commanded power profile that comes from ISO 118. The optimization problem is solved in a distributed way, where each distributed flexibility resource node exchanges data with the aggregator and performs one optimization iteration according to the distributed optimization scheme described herein to solve the global model predictive control optimization problem in a tractable fashion.
In the exemplary embodiment, the modified local cost function is the sum of a local cost (local utility function) and a Lagrange multiplier times the power consumed (supplied) by the distributed flexibility resource. The value of Lagrange multiplier 120 that is shared among nodes 102(1)-102(N) is calculated at the aggregator level. At aggregator 116, the Lagrange multiplier is a function of the commanded power profile and the local data that are sent to aggregator 116 at each iteration of optimization. The recalculated Lagrange multiplier is sent back to the nodes to be used in the cost for next iteration, and so on. As the nodes iterate synchronously, they converge towards an acceptable solution (that is, to a defined/predetermined convergence threshold) to the network/global Model Predictive Control optimization problem through the aforementioned iterative distributed computation scheme.
As represented in
Once initialized, the communications represented by labeled arrows three (3) and four (4) in
At the aggregator, at operation 310, using the data collected from the nodes and the commanded power profile from the independent service operator, the shared Lagrange multiplier is calculated, along with a step size. If convergence to the desired, defined level is not yet achieved (operation 312), the Lagrange multiplier and step size are sent to the nodes (arrow four (3) in
Using the new value of the Lagrange multiplier, a new search direction and an updated dataset is calculated by iteratively returning to step 306. The updated dataset is sent from controller 204(1) to aggregator 116 (arrow three (3) in
The initialization phase may, in one or more implementations, be started by aggregator 116 initializing itself and requesting that each controller 204 provide power feasible information (operation 402 of
For each load Li∈, a range (interval) [pmini,pmaxi] is assumed which defines the feasible values for power consumption. Hence, each of loads 110 sends its respective upper and lower limit of the power feasible range to aggregator 116 (operation 604 of
Operations 404, 406 and 408 of
where rj is the j-th element of r. The value of r is then sent to controllers 204, which may differ per controller. Note that the value of r corresponds to the target power obtained from the independent service operator, (operation 410), which may be obtained independently at any suitable time before the ratios are computed and sent (operations 412 and 414).
When the ratio vector is received by a controller (operation 606 of
The optimization phase is described herein with reference to the flow diagrams of
Each controller 204 starts with its own initial feasible primal Y0 (e.g., operation 700 of
At each load Li, operation 704 is performed, which calculates the step direction and also generates the quantities that represent the information matrix in condensed form. These per-node dependent values are sent to aggregator 116 by each controller 204 at operation 706 of
Aggregator 116, as represented by operation 512 of
At each controller 204, if ρk and αk are received (operations 708 and 710 of
Turning to another aspect, some loads have discrete power consumption, that is, are either on or off (in contrast to loads that accept continuous values for power control). Described herein is an algorithm that can handle loads that only accept boundary values of the power feasible interval (assuming the interval is the range from minimum to maximum). Thus, the power only accepts discrete values, e.g., pi∈{pmini,pmaxi} for the loads Li∈d⊂. A general goal here is to avoid mixed-integer programming, which is often not suitable for real time optimization, especially for large scale problems.
To this end, the optimization problem is first solved according to the algorithm assuming all the loads accept continuous values for power within the feasible interval. Let {
Via operations 802, 806 and 808, for each element j, which represents the j-th step in the prediction horizon, operation 804 sorts the sequence
and denotes it by {{circumflex over (P)}i}. Also, operation 804 denotes the corresponding sequence of the load numbers by I.
Operation 810 finds the maximum value of nj such that Σi=1n
Combining the continuous load optimization with the above-described discrete load post-processing allows the technology to handle any mixture of continuous and discrete loads.
In some embodiments, the distributed optimization scheme may be formulated as a predictive control problem, with one or more nodes 102 having individual and separate prediction horizons for model output and control action, referred to herein as a model prediction horizon and a control prediction horizon, respectively. The separation of the model prediction horizon from the control prediction horizon enables using relatively large model prediction horizons for slower nodes 102, while still maintaining the same control prediction horizon at aggregator 116. This keeps computational demands relatively low, and suitable for real-time applications. Further, this allows for different nodes 102 to have different model prediction horizons for different loads 110, and for a model prediction horizon for a particular node 102 to vary over time, as described herein.
In the systems and methods described herein, down-sampling is used to extend model prediction horizons for nodes 102. Specifically, suppose that the sample time is Ts, and that the control prediction horizon utilized by aggregator 116 has Np control steps. For each node 102, associated controller 104 uses a down-sampling factor Kp to modify the time value of each prediction step in the model prediction horizon for that node 102 to Kp times the sample time Ts. That is, each controller 104 solves its local optimization problem relative to a model prediction horizon that is discretized for an adjusted sample time Kp*Ts, instead of Ts. This permits extended prediction look-ahead at nodes 102 while still computing Np control steps, thereby keeping the computational burden relatively low. Specifically the model output prediction horizon is extended to Kp*Np*Ts minutes. Further, this down-sampling scheme is equivalent to employing a model discretized by the sample time Ts, but repeating each input value for Kp steps.
For example, assume the sampling time Ts is one minute, and assume the number of control steps Np is ten. Without using the down-sampling factor, the control prediction horizon utilized by aggregator 116 and the model prediction horizon used by a node 102 have the same length, ten minutes (i.e., one minute multiplied by ten steps). However, if a down-sampling factor Kp of three is used for the node 102, the control prediction horizon will still have a length of ten minutes, but the model prediction horizon for the node 102 will now be thirty minutes (i.e., one minute multiplied by ten steps, multiplied by the down-sampling factor of three).
Accordingly, each sample in the model prediction has a duration of Kp steps. The down-sampling factor is acceptable, and provides computational advantages, as long as a shortest time constant for asset response is still much greater than Kp*Ts.
The advantages of the down-sampling scheme are achievable with any positive integer valued model prediction horizon of Mp steps that is larger than the control prediction horizon of Np steps. However, to simplify the scheme, in the exemplary embodiment, the model prediction horizon steps Mp are an integer multiple (i.e., the down-sampling factor Kp) of the control prediction horizon steps Np. Accordingly, the predictive model for each node 102 is discretized using an adjusted sample time Kp*Ts, while the sample time utilized by aggregator 116 remains as Ts. The down-sampling factor Kp for each node 102 may be a predetermined value and be stored, for example, in associated controller 104. Alternatively, the down-sampling factor Kp for each node 102 may be dynamically calculated in real-time (e.g., using associated controller 104). Further the down-sampling factor Kp for each node 102 may change over time.
With control prediction horizon steps Np and model prediction horizon steps Mp, Mp=Kp*Np. Accordingly, the model prediction horizon is longer than the control prediction horizon. The distributed optimization algorithm still computes only Np control actions, but those actions impact over the model prediction horizon, which is Kp times longer than the control prediction horizon. This is equivalent to having a control prediction horizon having the extended length of the model prediction horizon, but with each control action applied for Kp consecutive samples.
For example, suppose the down-sampling factor Kp is three and the number of control prediction horizon steps Np is two. In this example, the following mathematical relationships may be defined: U=[u1 u2]T, Y=[y1, y2, . . . , y6]T, and U=[u1 u1 u1 u2 u2 u2]T, where U is a vector of future control actions to be computed by the distributed optimization algorithms, Y is a vector of future model outputs, and U is a fictitious equivalent future control action vector with the same horizon as the model prediction horizon. Accordingly, despite computing only two future control actions (i.e., because Np is two), with a Kp of three, node 102 solves its local optimization problem based on a model prediction horizon of six samples (i.e., because Mp is six). Although this is mathematically equivalent to computing a control action horizon with a sample length equal to the model prediction horizon (but constrained to the form of U), this scheme requires 1/Kp fewer computations, improving the suitability of the technique for large-scale, real-time applications.
As shown in
Relatively large changes in a commanded power level generally benefit from extended model prediction horizons to accommodate pre-cooling and pre-heating with sufficient lead time. However, controllers 104 must also maintain relatively short sample times to reduce reaction time and controller delay when a power setpoint change is received. Large model prediction horizons may add computational burdens, making them generally infeasible for large-scale real-time applications. However, the system and methods described herein address this problem by separating a control prediction horizon from model prediction horizons, and allowing model prediction horizons to differ for different loads 110 based on individual load dynamic response times.
For example, a relatively slow load 110 may take approximately two hundred minutes to adjust to a new power setpoint, whereas a relatively fast load 110 may take approximately ten minutes to adjust to a new power setpoint. Accordingly, it is generally desirable to have a longer model prediction horizon for a slower load 110 and a shorter model prediction horizon for a faster load 110. The systems and methods described herein allow for different loads 110 (and nodes 102) to have different model prediction horizons, allowing for longer model prediction horizons for slower loads 110 and shorter model prediction horizons for faster loads 110.
Operation of the down-sampling scheme described herein was simulated for one hundred loads 110 with large transitions in demanded target power. This is a relatively extreme case, both in terms of power demand levels and timing. For this simulation, the sample time Ts was one minute, the number of control prediction horizon steps Np was three, and the down-sampling factor Kp was ten (corresponding to a control prediction horizon of three minutes, and a model prediction horizon of thirty minutes). During the simulation, temperatures at loads 110 approached, but did not cross, lower boundaries. Accordingly, the simulation demonstrated successful operation of the systems and methods described herein.
The embodiments described herein include systems and methods for controlling a distributed power system that includes an aggregator communicatively coupled to a plurality of nodes, each of the plurality of nodes including an associated load. The aggregator receives a specified aggregated power level from an independent service operator. At each of a plurality of sample times recurring at a regular interval, the aggregator receives, from each of the plurality of nodes, a condensed dataset including load-specific information for that node. The aggregator calculates a global value based on the specified aggregated power level, the plurality of condensed datasets, and a control prediction horizon, wherein the control prediction horizon is a time period that is an integer multiple of the regular interval, and transmits the global value to each of the plurality of nodes. Each of the plurality of nodes solves a local optimization problem for that node based on the received global value and a local model prediction horizon for that node, wherein the local model prediction horizon for at least one node of the plurality of nodes is longer than the control prediction horizon, and controls the load based on the solved local optimization problem for that node.
An exemplary technical effect of the methods, systems, and apparatus described herein includes at least one of: (a) reducing power mismatch in response to ISO commanded power profiles; (b) reducing power tracking error; (c) improving compliance with regulatory requirements; and (d) improving power tracking in both synchronous and asynchronous systems.
Exemplary embodiments for controlling a distributed power system using a down-sampling scheme are described herein. The systems and methods of operating and manufacturing such systems and devices are not limited to the specific embodiments described herein, but rather, components of systems and/or steps of the methods may be utilized independently and separately from other components and/or steps described herein. For example, the methods may also be used in combination with other electronic systems, and are not limited to practice with only the electronic systems, and methods as described herein. Rather, the exemplary embodiment can be implemented and utilized in connection with many other electronic systems.
Although specific features of various embodiments of the disclosure may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the disclosure, any feature of a drawing may be referenced and/or claimed in combination with any feature of any other drawing.
This written description uses examples to disclose the embodiments, including the best mode, and also to enable any person skilled in the art to practice the embodiments, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the disclosure is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.
This invention was made with Government support under contract number DE-AR0000698 awarded by ARPA-E. The Government has certain rights in this invention.