Electric grid load forecasts with distributed photovoltaic generation
In the context of an electrical utility system, changing cloud conditions may cause a customer having solar panels to greatly increase or decrease electrical demand in a difficult-to-predict manner. Accordingly, a spinning reserve maintained by an electric utility company must be larger, and is therefore more expensive. In an example, the spinning reserve may be managed by: calculating a stable sequence of forecasts of smoothed real-time consumption, wherein the calculating is based at least in part on smoothed estimates of consumption data. A stable sequence of forecasts of real-time measured load may be calculated by subtracting forecasts of real-time distributed solar photovoltaic (PV) generation data from the stable sequence of forecasts of smoothed real-time consumption. The spinning reserve of the electricity system may be controlled based at least in part on the stable sequence of forecasts of real-time measured load.
1 . A method of managing a spinning reserve of an electricity grid, comprising:
deriving weighted smoothed estimates of distributed solar photovoltaic (PV) generation data, wherein the deriving the weighted smoothed estimates of distributed solar PV generation data comprises:
deriving smoothed estimates of distributed solar PV generation data by applying smoothing methods to estimates of PV generation data; and
applying normalized similarity weights to the smoothed estimates of distributed solar PV generation data to form the weighted smoothed estimates of distributed solar PV generation data;
deriving a stable sequence of forecasts of smoothed real-time consumption, wherein the deriving the stable sequence of forecasts of smoothed real-time consumption is based at least in part on smoothed estimates of consumption data, and wherein the smoothed estimates of consumption data are derived by actions comprising:
applying smoothing methods and normalized similarity weights to derive estimates of consumption; and
deriving a stable sequence of forecasts of real-time measured consumption by subtracting forecasts of the distributed solar PV generation data from the stable sequence of forecasts of smoothed real-time consumption; and
managing the spinning reserve of the electricity grid based at least in part on the stable sequence of forecasts of real-time measured consumption, wherein the managing comprises:
responsive to deriving the stable sequence of forecasts of real-time measured consumption, reducing the spinning reserve; and
responsive to forecast instability, increasing spinning reserves.
2 . The method of claim 1 , wherein the forecasts of real-time the distributed solar PV generation data are derived by actions comprising:
combining forecasts of global horizontal solar irradiance with estimates of installed distributed solar PV capacity data to form the forecasts of real-time the distributed solar PV generation data.
3 . The method of claim 1 , wherein the smoothing methods applied to the estimates of PV generation data and the normalized similarity weights applied to the smoothed estimates of distributed solar PV generation data were also used for deriving the smoothed estimates of consumption data.
4 . The method of claim 1 , wherein the normalized similarity weights applied to the smoothed estimates of distributed solar PV generation data are based at least in part on estimates of distributed solar PV generation data under clear sky conditions.
5 . The method of claim 1 , additionally comprising:
deriving estimates of consumption as a sum of measured consumption data and estimates of distributed solar PV generation data.
6 . The method of claim 5 , additionally comprising:
obtaining the measured consumption data;
obtaining the estimates of distributed solar PV generation data;
obtaining estimates of installed distributed solar PV capacity data;
obtaining estimates of distributed solar PV generation data under clear sky conditions; and
obtaining forecasts of global horizontal solar irradiance.
7 . A system to manage a spinning reserve of an electricity grid, comprising:
a processor; and
a memory device, in communication with the processor, wherein the memory device contains instructions comprising:
deriving weighted smoothed estimates of distributed solar photovoltaic (PV) generation data, wherein the deriving the weighted smoothed estimates of distributed solar PV generation data comprises:
deriving smoothed estimates of distributed solar PV generation data by applying smoothing methods to estimates of PV generation data; and
applying normalized similarity weights to the smoothed estimates of distributed solar PV generation data to form the weighted smoothed estimates of distributed solar PV generation data; and
deriving a stable sequence of forecasts of smoothed real-time consumption, wherein the deriving the stable sequence of forecasts of smoothed real-time consumption is based at least in part on smoothed estimates of consumption data, and wherein the smoothed estimates of consumption data are derived by actions comprising:
applying smoothing methods and normalized similarity weights to derive estimates of consumption;
deriving a stable sequence of forecasts of real-time measured consumption by subtracting forecasts of the distributed solar PV generation data from the stable sequence of forecasts of smoothed real-time consumption; and
managing the spinning reserve of the electricity grid based at least in part on the stable sequence of forecasts of real-time measured consumption, wherein the managing comprises:
responsive to deriving the stable sequence of forecasts of real-time measured consumption, reducing the spinning reserve; and
responsive to forecast instability, increasing spinning reserves.
8 . The system of claim 7 , wherein the instructions further comprise deriving the forecasts of real-time the distributed solar PV generation data, which comprises:
combining forecasts of global horizontal solar irradiance with estimates of installed distributed solar PV capacity data to form the forecasts of real-time the distributed solar PV generation data.
9 . The system of claim 2 , wherein the smoothing methods applied to the estimates of PV generation data and the normalized similarity weights applied to the smoothed estimates of distributed solar PV generation data were also used for deriving the smoothed estimates of consumption data.
10 . The system of claim 7 , wherein the normalized similarity weights applied to the smoothed estimates of distributed solar PV generation data are based at least in part on estimates of distributed solar PV generation data under clear sky conditions.
11 . One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by one or more processors, configure a computing device to perform acts comprising:
deriving weighted smoothed estimates of distributed solar photovoltaic (PV) generation data, wherein the deriving the weighted smoothed estimates of distributed solar PV generation data comprises:
deriving smoothed estimates of distributed solar PV generation data by applying smoothing methods to estimates of PV generation data;
applying normalized similarity weights to the smoothed estimates of distributed solar PV generation data to form the weighted smoothed estimates of distributed solar PV generation data; and
deriving a stable sequence of forecasts of smoothed real-time consumption, wherein the deriving the stable sequence of forecasts of smoothed real-time consumption is based at least in part on smoothed estimates of consumption data, and wherein the smoothed estimates of consumption data are derived by actions comprising:
applying smoothing methods and normalized similarity weights to derive estimates of consumption;
deriving a stable sequence of forecasts of real-time measured consumption by subtracting forecasts of the distributed solar PV generation data from the stable sequence of forecasts of smoothed real-time consumption; and
managing a spinning reserve of an electricity grid based at least in part on the stable sequence of forecasts of real-time measured consumption, wherein the managing comprises:
responsive to deriving the stable sequence of forecasts of real-time measured consumption, reducing the spinning reserve; and
responsive to forecast instability, increasing spinning reserves.
12 . The one or more non-transitory computer-readable media of claim 11 , wherein the acts performed by the computing device further comprise deriving the forecasts of real-time the distributed solar PV generation data, which comprises:
combining forecasts of global horizontal solar irradiance with estimates of installed distributed solar PV capacity data to form the forecasts of real-time the distributed solar PV generation data.
13 . The one or more non-transitory computer-readable media of claim 11 , additionally comprising:
wherein the smoothing methods applied to the estimates of PV generation data and the normalized similarity weights applied to the smoothed estimates of distributed solar PV generation data were also used for deriving the smoothed estimates of consumption data.