Methods and systems for operating energy management systems

Abstract

A method and system for operating an energy management system (EMS) for a microgrid operates to automatically determine weights with which different objective functions are weighted in a multi-objective optimization performed by the EMS.

Description

[Technical Field]

[0001] Field of Invention Embodiments of the present invention relate to methods, apparatus, and systems for operating an energy management system for a microgrid. Embodiments of the present invention relate, in particular, to methods, apparatus, and systems that enable the adjustment of the operation of an energy management system. [Background technology]

[0002] Background of the Invention A microgrid is a localized group comprising energy-generating assets (such as renewable energy resources and / or generators), loads, and optional energy storage systems. Microgrid control strategies are becoming increasingly important, partly due to the growing use of other systems with renewable energy sources (RES), energy storage systems (ESS) such as battery energy storage systems (BESS), or distributed energy generators (DEG). Microgrid control techniques are described, for example, in IEEE 2030.7-2017.

[0003] A microgrid control system may be a Power Management System (PMS) capable of adjusting multiple individually controllable power generation assets and discretionary load (DL) assets in a predetermined manner. The operating point (OP) of each asset can be calculated in real time based on locally known values ​​such as total load, microgrid configuration, state of charge (SoC), current photovoltaic (PV) and wind availability, and other disadvantage factors. In such a case, the optimization that can be achieved may be limited because the PMS only knows local values ​​and has only historical and current time data.

[0004] To further improve the determination of the operating point, an Energy Management System (EMS) can use forecasts to calculate a better optimal operating point (OP) for each asset. The forecasts may include, or may not include, predicted values ​​such as load profiles, photovoltaic and wind availability, weather and cloud forecasts, and other disadvantage factors. Using historical, current, and forecast data, the EMS is adapted to calculate the optimal OP for each asset. This may be done over a forecast period.

[0005] An EMS can perform multi-objective optimization (MOO) to determine the asset operating point over the forecast period. The weights assigned to the various objective functions in MOO can be set based on expert knowledge. This human involvement can be prone to errors. For example, the weights in MOO may be selected by a human expert who is unsuitable for at least some scenarios. [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] overview Considering the above, there is a continued need for improved methods, devices, systems, and microgrids that enable reliable determination of asset operating points. There is also a need for methods, devices, systems, and microgrids that enable the adjustment of energy management system (EMS) operation. Furthermore, there is a need for methods, devices, systems, and microgrids that enable the adjustment of EMS operation according to the use case in which the EMS is intended to be used. Additionally, there is a need for methods, devices, systems, and microgrids that enable the adjustment of EMS operation based on objective criteria without requiring human expert input. [Means for solving the problem]

[0007] According to the present invention, methods and systems according to the independent claims are provided. Dependent claims define preferred embodiments.

[0008] Embodiments of the present invention provide a method and system for automatically adjusting an energy management system (EMS) for an electric microgrid. The EMS can employ a numerically optimized energy management technique that can plan the resource allocation of the microgrid to minimize a composite objective function. Available resources may include the possibility of buying and selling energy between the microgrid and the main grid to which it is connected, e.g., locally dispatchable generators using fossil fuels, renewable energy systems such as wind and solar PV, controllable loads, and energy storage systems such as batteries. The selected objective function takes into account several performance metrics, such as penalties imposed by energy exchanged with the main grid, penalties imposed by shifting loads against the nominal consumption profile, and cumulative wear of the storage system.

[0009] According to one embodiment, the weights by which different objective functions are multiplied in the composite objective function are automatically determined and used by the EMS.

[0010] According to one aspect of the present invention, a method of operating an Energy Management System (EMS) for a microgrid is provided. The EMS performs multi-objective optimization (MOO) to determine one or more asset operating points over a prediction period. Performing MOO comprises determining one or more asset operating points as a function of time over the prediction period that minimize a composite objective function that is a weighted sum of a plurality of objective functions.

[0011] The method may comprise using MOO in the on-site use of the EMS.

[0012] The method may comprise using MOO to perform corrective or preventive actions in a power system.

[0013] The method may comprise performing at least one protective action using MOO.

[0014] The method may comprise using MOO to control an output interface.

[0015] The method may comprise controlling an output interface using MOO to output alarms, warnings, status information, or other power system-related information.

[0016] The method comprises, by at least one integrated circuit, automatically determining weights with which a plurality of objective functions are weighted in the composite objective function used by the EMS in the MOO.

[0017] In MOO, automatically determining the weights to which the objective function is weighted in a composite objective function used by EMS includes, using at least one integrated circuit, determining a set of utopian values, each of which corresponds to one optimal value of several objective functions when optimized independently of other objective functions; determining a Pareto front for the optimal solution, each of which optimizes a different weighted sum of several objective functions; and determining the weights to which the objective function is weighted based on the Pareto front and the set of utopian values.

[0018] The term "utopia value" refers to the value of one of several optimized objective functions when optimization is performed independently of other objective functions included in the composite objective function. Therefore, the "utopia value" is the optimal value of only one objective function when optimized while ignoring the other objective functions included in the composite objective function. When MOO is performed, the contribution of an objective function to the optimal value of the composite objective function is greater than the utopia value of this objective function because MOO considers several competing objectives.

[0019] Determining weights based on a Pareto front and a set of utopian values ​​may involve determining a utopian point having coordinates defined by the set of utopian values.

[0020] Determining weights based on a set of Pareto fronts and utopian values ​​may involve determining the distance of utopian points from points on the Pareto front.

[0021] Determining weights based on a set of Pareto fronts and utopian values ​​may involve determining a point on the Pareto front that has the minimum distance from a utopian point.

[0022] In a composite objective function, the weights to which the objective functions are weighted can be determined based on the weights applied to several objective functions when determining a point on the Pareto front that has the minimum distance from the utopian point.

[0023] Determining a utopian value may involve determining a first utopian value that represents the minimum value of a first objective function related to energy generation in the grid to which the microgrids are connected, and determining a second utopian value related to local energy generation in the microgrids.

[0024] A second utopian value related to local energy generation may include efflux effects.

[0025] Determining the weights may involve determining a first weight, which is multiplied in the composite objective function by a first objective function related to energy generation in the grid to which the microgrids are connected, and a second weight, which is multiplied in the composite objective function by a second objective function related to local energy generation in the microgrids.

[0026] Determining the weights may involve determining a third weight multiplied by a third objective function related to the energy storage system of the microgrid, particularly the battery energy storage system BESS.

[0027] The second objective function may include emission effects.

[0028] Determining the utopian value and determining the Pareto front may each involve performing optimization under constraints.

[0029] The constraints may include one or more of the following: power balancing of the microgrid under a given scenario, consistency of the load profile, and / or power generation.

[0030] The constraints may include technical limitations of microgrid assets.

[0031] The constraints may include the preferences of the microgrid operator.

[0032] Determining the utopian value, determining the Pareto front, and determining the weights may be performed separately for each of several different scenarios.

[0033] Different scenarios can be distinguished from one another with respect to the microgrid load and / or power generation profile.

[0034] This method may include clustering data acquired from multiple microgrids to identify multiple use cases.

[0035] The weights used by the EMS may be selected based on the microgrid specifications in comparison to the use case.

[0036] Automatically determining weights can be done for at least one of the use cases that can be selected based on the microgrid specifications.

[0037] This method may further involve using EMS to perform MOO to determine one or more asset operating points as a function of time over the forecast period, where the objective function within MOO depends on the determined weights.

[0038] The forecast period may include at least 6 hours, at least 12 hours, at least 18 hours, at least 24 hours, or at least 48 hours.

[0039] This method may further include controlling at least one asset of the microgrid in accordance with the determined asset operating point.

[0040] This method may further include providing the operating point to the power management system (PMS) via an EMS.

[0041] This method may include using a PMS to control controllable assets in a microgrid according to operating points determined by an EMS.

[0042] In another embodiment, a system for controlling the operation of an EMS is provided. The EMS operates to perform a Moment of Operation (MOO) to determine one or more asset operating points over a forecast period, the MOO determining one or more asset operating points as a function of time over the forecast period that minimizes a composite objective function, which is a weighted sum of several objective functions. The system comprises at least one integrated circuit that operates to automatically determine the weights to which the objective functions are weighted in the composite objective function used by the EMS in the MOO, and an interface for providing the determined weights to the EMS.

[0043] The system can operate to perform a method according to one embodiment.

[0044] The system can operate using MOO to perform corrective or preventive actions in the power system.

[0045] The system can operate to perform at least one protective action using MOO.

[0046] The system can operate to control the output interface using MOO.

[0047] The system can use MOO to control the output interface and operate to output alarms, warnings, status information, or other power system-related information.

[0048] A microgrid according to one embodiment comprises a plurality of controllable assets and an EMS that operates to perform a MOO to determine one or more asset operating points over a forecast period, wherein the MOO determines one or more asset operating points as a function of time over the forecast period that minimizes a composite objective function which is a weighted sum of several objective functions, and a system for controlling the operation of the EMS.

[0049] The microgrid may optionally have one or more loads.

[0050] Multiple controllable assets may include controllable power generation assets and / or controllable loads such as any controllable load.

[0051] Multiple controllable assets may include renewable energy sources.

[0052] Multiple controllable assets may include arbitrary loads.

[0053] Multiple controllable assets may optionally include one or more generators and / or one or more energy storage systems (ESS).

[0054] ESS can be equipped with a battery ESS (BESS).

[0055] Multiple controllable assets can form a decentralized energy generation system (DEG).

[0056] Microgrids may have additional assets that are not controlled by the EMS.

[0057] Additional assets can be detected and may have operating parameters that affect at least one of the objective functions within MOO.

[0058] Microgrids can be equipped with PMS (Protection Management System).

[0059] A separate device that is communicatively coupled to the EMS and / or to the EMS may operate to perform the methods according to the various embodiments disclosed herein.

[0060] Microgrids can operate using MOO to perform corrective or preventive actions in power systems.

[0061] A microgrid can operate using MOO to perform at least one protective action.

[0062] A microgrid can operate using MOO to control its output interface.

[0063] A microgrid can use MOO to control its output interface and operate to output alarms, warnings, status information, or other power system-related information.

[0064] Various effects can be achieved using the methods and control systems according to the embodiments. The methods and control systems according to the embodiments address the need for improved reliability and repeatability in determining the operating parameters of a microgrid. The methods and control systems according to the embodiments allow for tuning the operation of the EMS depending on the use case in which the EMS is used (e.g., the details of the microgrid).

[0065] Brief explanation of the drawing The subject matter of the present invention will be described in more detail with reference to preferred exemplary embodiments shown in the accompanying drawings. [Brief explanation of the drawing]

[0066] [Figure 1] This is a schematic diagram of a microgrid. [Figure 2] This is a schematic diagram of a microgrid. [Figure 3] This is a schematic diagram of a microgrid. [Figure 4] This demonstrates the operation of a microgrid system. [Figure 5] This is a flowchart of the method. [Figure 6] The Pareto front and the utopia point are shown. [Figure 7] This is a flowchart of the method. [Modes for carrying out the invention]

[0067] Detailed description of the embodiment Exemplary embodiments of the present invention will be described with reference to drawings in which the same or similar reference numerals indicate the same or similar elements. Some embodiments will be described in the context of the concept of an exemplary charging infrastructure and / or an exemplary on-board battery, but the embodiments are not limited thereto. Features of the embodiments can be combined with each other unless otherwise specified.

[0068] Embodiments of the present invention can be used to provide improved robustness and adaptability in microgrid control. The operation of the energy management system (EMS) can be adjusted by adjusting the weights used in multi-objective optimization (MOO). The adjustment may be performed automatically using at least one integrated circuit. The weights may be determined depending on the use case in which the EMS is deployed. The weights may be determined by:

[0069] Figure 1 shows an exemplary microgrid 10 comprising several controllable power generation assets 11, 12, 13, and 14. The microgrid 10 may further include one or more loads 18 which may comprise one or more controllable loads 15, 16, and 17. One or more controllable loads 15, 16, and 17 may comprise one or more arbitrary loads. Power generation within the microgrid 10 is controlled by a control system which includes a power management system (PMS) 40 and / or an energy management system (EMS) 50.

[0070] The microgrid 10 may be connected to a macrogrid (also called a main grid). The microgrid 10 may include circuit breakers or other disconnectors for controllably connecting and disconnecting the microgrid from the macrogrid.

[0071] The multiple controllable power generation assets 11, 12, 13, and 14 may include renewable energy sources such as wind turbines shown in Figure 1 or photovoltaic modules 21, 22, and 23 shown in Figure 2. The multiple controllable power generation assets may also include gas turbines or other generators that operate on fossil fuels or energy storage systems (ESS).

[0072] The multiple controllable loads 15, 16, and 17 may include any load.

[0073] Multiple controllable power generation assets 31, 32, and 33 are generally shown as blocks in Figure 3, and it is understood that controllable power generation assets may include wind turbines, photovoltaic modules, other renewable energy sources (RES), fossil fuel-consuming generators, or ESS.

[0074] The PMS40 can control and adjust individual assets, particularly controllable power generation assets and / or controllable loads. The PMS40 can operate using locally known parameters such as total load, microgrid configuration, state of charge (SoC), current PV and wind availability, or other penalty factors for control purposes. The PMS40 may also include an optimization engine that optimizes its operation based on available data. This optimization may be limited because the PMS40 typically only has access to locally available values ​​as well as historical and current time data.

[0075] EMS50 may include an optimization system that uses historical and current local data, in addition to forecasts, to calculate a better optimal operating point for each asset. Forecasts may include load profiles, PV and wind availability, weather and cloud forecasts, and other penalty factors.

[0076] The EMS50 may use the measured parameters when performing MOO to determine the operating point. The measured parameters may include one or more measurements of parameters relating to the electrical properties and / or fluid properties of at least one asset of the microgrid (e.g., transformer insulating fluid, ambient air temperature, ambient air humidity, ambient wind speed, etc.) and / or other parameters (e.g., mean solar radiation).

[0077] The PMS40 can use measured parameters when performing corrective and / or preventive control actions within the microgrid. Measured parameters may include one or more measurements of parameters relating to the electrical properties and / or fluid properties (e.g., transformer insulating fluid, ambient air temperature, ambient air humidity, ambient wind speed, etc.) and / or other parameters (e.g., mean solar radiation) of at least one asset in the microgrid.

[0078] The PMS40 and / or EMS50 may be communicatively coupled to a plurality of sensors 26, 27 collectively referred to as sensor 25, which measure parameters and provide the measurements to the PMS40 and / or EMS50. The plurality of sensors 25 can operate to measure parameters relating to the assets of the microgrid, components connected to the assets, and / or ambient parameters. The plurality of sensors 25 can operate to measure one or more of the following parameters relating to the electrical properties of at least one asset of the microgrid, and / or parameters relating to the fluid properties (e.g., transformer insulating fluid, ambient air temperature, ambient air humidity, ambient wind speed, etc.), and / or other parameters (e.g., mean solar radiation).

[0079] The EMS50 may be connected to forecast servers 61a, 61b, and 61c via a wide-area network 60. The forecast servers may include weather forecast servers, energy availability forecast servers, load profile forecast servers, or other forecast servers.

[0080] The EMS50 can perform optimization procedures to determine the optimal operating points of multiple controllable assets, particularly the controllable power generation assets and / or controllable loads of the microgrid 10. The EMS50 may also perform MOO. MOO may include determining the operating points as a function of time over a forecast period (e.g., 24 hours), which is a composite objective function.

[0081]

number

[0082] Objective function J jmay depend not only on the operating point, but also, for example, on measurement parameters and / or other parameters that can be obtained via a user interface. Then, assuming that optimization is performed on the measured parameters, optimization is performed using the operating point as a variable to be optimized. For illustrative purposes, slowly changing measurements such as operating characteristics (e.g., wear) of a power transformer and / or a point of common coupling may be considered in one or more of the objective function J j .

[0083] Using past, present and future (predicted) data, the EMS calculates the best or optimal operating point for each asset, which may be in the form of a power setpoint (e.g., for a generator, ESS, etc.), a power limit (e.g., for PV, wind turbine, etc.), or a load power setpoint (e.g., for any load). This set of operating points is "optimal" in that it results in an improved metric. Examples of improved metrics include, but are not limited to, reduction of fossil fuel consumption, reduction of CO₂ emissions, reduction of maintenance time, and reduction of downtime. The composite objective function of equation (1) is a weighted sum of metric values representing such objectives.

[0084] A system 60, which may be separate or integrated from the system on which the EMS 50 runs, operates to set the weight w multiplied by the objective function to determine the composite objective function J i . The computing system 60 may comprise, or be communicatively coupled to, a database 63 containing historical data representing EMS use cases and operational scenarios. The computing system 60 may operate to determine the weight w multiplied by the objective function for at least one use case similar to the use case for which the EMS 50 is deployed, so as to determine the composite objective function J i .

[0085] This means that, (i) under the constraint that the sum of all weights w i equals 1, the weight w i(ii) the Pareto front of the optimal solution obtained for equation (1) for the optimization problem of various parameterizations of and (ii) the objective function J j This can be done automatically based on a utopia point calculated by individually optimizing two, three or more, or all of these factors.

[0086] The term "utopia point" refers to a point in the operating point space where one of the objective functions is optimal (e.g., minimal) when optimized individually from the other objective functions included in the composite objective function.

[0087] Determining a "utopia point" for the objective function can involve determining the operating point as a function of time over the forecast period (e.g., 24 hours), which is the objective function

[0088]

number

[0089] The value of an objective function is also called a utopian value when it is optimized (e.g., minimized) independently of other objective functions. The term utopian value refers to one of several optimized objective functions when optimization is performed independently of other objective functions included in the composite objective function. Therefore, the utopian value is the optimal value of only one objective function when optimized while ignoring the other objective functions included in the composite objective function. When MOO is performed, the contribution of an objective function to the optimal value of the composite objective function is greater than the utopian value of this objective function because MOO considers several competing objectives.

[0090] Determining a set of utopia values ​​and / or utopia points may include determining a first utopia value and / or first utopia point where the objective function related to energy transfer to and from the main grid (such as the penalty imposed when energy is supplied from the main grid to the microgrid) is minimized. Determining a set of utopia values ​​and / or utopia points may include determining a second utopia value and / or second utopia point where the objective function related to power generation in the microgrid (such as the penalty imposed when fossil fuels must be used in the microgrid for power generation, and this penalty may include a penalty for carbon dioxide emissions) is minimized. Determining a set of utopia values ​​and / or utopia points may include determining a third utopia value and / or third utopia point where the objective function related to ESS wear (such as the penalty imposed on the BESS charging and discharging process that causes wear) is minimized.

[0091] The system 60 includes one or more integrated circuits 61. The integrated circuits 61 may be implemented as a processor, microprocessor, controller, microcontroller, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or a combination thereof.

[0092] One or more integrated circuits 61 may be configured to run an optimizer 62 using appropriate hardware, firmware, and software. System 60 (i) Objective function J j To determine the utopia point calculated by individually optimizing two, three or more, or all of them, several times, (ii) (all weights w i (Under the constraint that the sum of the weights is equal to 1) iThe optimizer 62 may be configured to run several times to determine the Pareto front of the optimal solution obtained for equation (1) for various parameterization optimization problems.

[0093] Optimization can identify the operating point where each individual objective function (for utopian point determination) or composite objective function (for Pareto front generation) is optimal (typically minimal). Optimization uses these minimum values ​​of the objective functions (for utopian point determination) or composite objective functions (for Pareto front generation), and weights w for various points on the Pareto front. i Each parameterization can be identified.

[0094] Optimization may be performed under constraints. These constraints may include one or more of the following: power balance of the microgrid under a given scenario, consistency of load profiles, and / or power generation. Constraints may include technical constraints on microgrid assets. Constraints may include microgrid operator preferences that can be entered via a user interface.

[0095] As will be explained in more detail below, IC61 is the objective function J i The weights w are used in the composite objective function used by EMS50 in MOO. i This involves determining a set of utopian values, each corresponding to the optimal value of one of several objective functions when optimized independently of other objective functions; determining the Pareto front of the optimal solution, each of which determines a Pareto front that optimizes a different weighted sum of several objective functions; and determining the objective function J based on the Pareto front and the set of utopian values. i The weights that are weighted lol i By making a decision, it can be operated to make an automatic decision.

[0096] Using distance-based techniques, we can identify the point on the Pareto front that is closest to the utopian value.

[0097] weight w i The weights w may be provided to EMS50 for use in executing MOO. EMS50 may perform MOO such that the composite objective function according to equation (1) is minimized, and the weights w i This is determined by the system 60. The optimization performed by the EMS 50 may depend on measurements captured by multiple sensors 25.

[0098] Figure 4 is a schematic block diagram illustrating the operation of the microgrid control system.

[0099] System 60 is the weight used by EMS50 in MOO w i This is determined automatically based on the use case in which the EMS50 will be deployed.

[0100] System 60 is weight w i The weights are provided to the EMS50. The weights may be transmitted via a wired or wireless interface. i This can be adjusted during the commissioning of the microgrid. Weight w i This can be adjusted during field operation of the EMS50 microgrid.

[0101] EMS50 is heavy w i Received and provided weight w i MOO is performed on a composite objective function in which several objective functions are weighted. The results of MOO include asset operating points. EMS50 provides asset operating points to PMS40. This may be done in a look-ahead manner, for example, over forecast periods including 6 hours or more, 12 hours or more, 24 hours or more, and 48 hours or more.

[0102] The forecast period has a specific granularity and includes integer-shorter time intervals. These shorter time intervals may be the repetition intervals for data sampling (e.g., from a measuring device) and / or the repetition intervals for calculations by EMS50 or PMS40.

[0103] The PMS40 controls the microgrid assets according to the received operating point.

[0104] Systems 60, EMS50, and / or PMS40 may be implemented on the same hardware system.

[0105] Figure 5 is a flowchart of Method 70. Method 70 may be performed automatically by System 60.

[0106] In step 71, load and power generation profiles can be retrieved from the database. The database may contain historical and / or composite profiles. The retrieved load and power generation profiles may depend on the microgrid specification, i.e., the use case in which the EMS50 is deployed.

[0107] In step 72, the objective function J when individually optimized. i To determine the utopian value corresponding to the minimum value of the objective function J, several optimizations are performed under constraints. The utopian value is the objective function J i Each of these may be determined, but is not required. For illustrative purposes, it may be known that the optimal value of the objective function related to local power generation in a microgrid may be zero (corresponding to the case where fossil fuels are not locally consumed in the microgrid). In such cases, the optimization routine for determining the utopian value may be limited to an objective function known to have a non-zero minimum (such as a penalty for energy transfer from the main grid to the microgrid).

[0108] In step 73, the Pareto front of the optimal solution is calculated. This is calculated using the weights w. iThis may involve determining the value of the composite objective function according to equation (1) for different parameterizations. The calculation of the Pareto front may involve, for example, the weights w selected on the grid. i This can be performed for various combinations of the following. The Pareto front calculation may also be performed for at least two independently variable weights w1 and w2, where weights w1 and w2 may be multiplicative weight coefficients applied to an objective function relating to energy transfer between the main grid and local power generation within the microgrid, respectively. Then, a third weight w3 relating to ESS wear can be w3 = 1 - w1 - w2, due to the fact that the sum of the weights is constrained to be equal to 1.

[0109] In step 74, weights used in the MOO performed by EMS50 are selected based on both the Pareto front and utopian values. Determining weights based on a set of Pareto front and utopian values ​​may include determining the distance of a utopian point from a point on the Pareto front. Determining weights based on a set of Pareto front and utopian values ​​may include determining a point on the Pareto front that has the minimum distance from a utopian point. The weights to which the objective function is weighted in the composite objective function may be determined based on the weights applied to several objective functions when determining a point on the Pareto front that has the minimum distance from a utopian point.

[0110] Figure 6 shows the Pareto front 80 and the utopia point 81. The utopia point 81 is determined by various individual objective functions J determined by the utopia value. iIt may have coordinates in space spanned by the value of . For illustrative purposes, one of the coordinates of utopia point 81 may be a first utopian value representing the minimum value of the first objective function when the first objective function related to energy generation in the grid to which the microgrid is connected is minimized independently of other objective functions (but under constraints such as power balance and other technical constraints). Another one of the coordinates of utopia point 81 may be a second utopian value representing the minimum value of the second objective function related to local energy generation in the microgrid when the second objective function is minimized independently of other objective functions (but under constraints such as power balance and other technical constraints). Yet another one of the coordinates of utopia point 81 may be a third utopian value representing the minimum value of the third objective function related to ESS wear when the third objective function is minimized independently of other objective functions (but under constraints such as power balance and other technical constraints). A second utopian value related to local energy generation may include emission effects, such as penalties imposed on increasingly undesirable fossil fuel consumption.

[0111] Point 82 on the Pareto front 80 is determined based on both the Pareto front 80 and the utopia point 81. For illustrative purposes, point 82 may be determined to be a point on the Pareto front 80 that is at the minimum distance (calculated according to appropriate distance metrics such as L1, L2, etc.) from the utopia point.

[0112] The weight that determines point 82 on the Pareto front closest to the utopia point w i This may be identified as the weight used by EMS50 in MOO.

[0113] Weights used by EMS50 in MOO i Other techniques may be used to make this determination. For explanatory purposes, a balanced or regularized selection can be made.

[0114] The use cases and / or scenarios used by System 60 to determine the weights used by EMS50 in MOO may be generated systematically from historical and / or synthetic data. Figure 7 is an exemplary flowchart of the techniques to which clustering and classification are applied.

[0115] Figure 7 is a flowchart of method 90. Method 90 may be performed automatically by system 60.

[0116] In step 91, database 6 3 Data is read from. The data may include historical use cases and / or operating scenarios. The data may include synthetic use cases and / or operating scenarios. For illustrative purposes, machine learning (ML) techniques can be used to generate a synthetic load and / or power generation profile for a microgrid. The ML model used to generate the synthetic load and / or power generation profile may have an input layer that receives historical operating scenarios. The ML model may have one or more hidden layers. The ML model may have an output layer that outputs the synthetic load and / or power generation profile.

[0117] In step 92, clustering and classification can be performed to identify similar use cases and / or load and power generation profiles. Clustering may include k-means clustering or other clustering techniques.

[0118] In step 93, a use case is selected. If it is already known which EMS50 should be weighted, the use case may be selected based on the intended deployment scenario of the EMS50. For illustrative purposes, a use case may be selected that has the same or similar types of renewable energy resources as the microgrid in which the EMS50 is deployed, or has similar average power generation and / or load values.

[0119] In steps 71-74, the weights are determined. This can be done as described with reference to Figure 5. The selected specific use case determines the loads and / or power generation profiles and constraints imposed when determining the utopia value and Pareto front.

[0120] In step 94, verification can be performed. Verification may include, for example, a simulation in which the MOO when run using the determined weights is checked against historical data known to represent an effective control strategy.

[0121] Steps 93, 71-74, and 94 can be repeated. For example, if several similar use cases are available, weights can be determined for some of the use cases. A set of weights that are evaluated as superior to others in the verification in step 94 may be deployed to the EMS50. Alternatively or additionally, sets of weights may be proactively generated for various use cases, either during commissioning or in field use, and stored for deployment to the EMS50.

[0122] Various effects are associated with the methods and systems according to the embodiments. For example, the methods and systems allow for the adjustment of MOO parameters performed by the EMS. This can be done automatically and based on objective criteria. Commissioning and / or reconfiguration of the EMS becomes easier.

[0123] Although the present invention has been described in detail in the drawings and the foregoing description, such description should be considered descriptive or illustrative and not restrictive. Variations of the disclosed embodiments can be understood and achieved by those skilled in the art from a study of the drawings, disclosure and the appended claims, and by practicing the claimed invention. In the claims, the word “comprising” does not exclude other elements or processes, and the indefinite article “a” or “an” does not exclude plurals. The mere fact that certain elements or processes are described in separate claims does not indicate that combinations of these elements or processes cannot be used advantageously, and specifically, any further meaningful combination of claims should be considered disclosed in addition to the dependency of the actual claims.

Claims

1. A method for operating an energy management system (EMS) for a microgrid, The EMS performs multi-objective optimization (MOO) to determine one or more asset operating points over the forecast period, and performing the MOO determines the one or more asset operating points as a function of time over the forecast period, minimizing a composite objective function which is a weighted sum of several objective functions. The method includes, by at least one integrated circuit, automatically determining the weights to which several objective functions are weighted in the composite objective function used by the EMS in the MOO, In the MOO, automatically determining the weights to which the objective function is weighted in the composite objective function used by the EMS is performed using at least one integrated circuit, namely, Determining a set of utopian values, wherein each of the utopian values ​​corresponds to one optimal value among several objective functions when optimized independently of other objective functions, Determining the Pareto front of the optimal solution, wherein each of the optimal solutions optimizes the different weighted sums of the several objective functions, Based on the Pareto front and the set of utopian values, the weights to which the objective function is weighted are determined. Includes, The aforementioned method, This further includes clustering data acquired from multiple microgrids to identify multiple use cases, A method for automatically determining the aforementioned weights, which is performed for at least one of the aforementioned use cases.

2. The method according to claim 1, wherein determining the weights based on the Pareto front and the set of utopia values ​​includes determining a utopia point having coordinates defined by the set of utopia values.

3. The method of claim 2, wherein determining the weights based on the set of Pareto fronts and utopia values ​​includes determining the distance of the utopia points from points on the Pareto fronts.

4. The method according to claim 2 or 3, wherein determining the weights based on the set of Pareto fronts and utopia values ​​includes determining a point on the Pareto front having the minimum distance from the utopia point.

5. The method according to claim 4, wherein the weights to which the objective functions are weighted in the composite objective function are determined based on weights applied to several objective functions when determining the point on the Pareto front having the minimum distance from the utopia point.

6. The method according to any one of claims 1 to 5, wherein determining the utopia value includes determining a first utopia value that represents the minimum value of a first objective function related to energy generation in the grid to which the microgrid is connected, and determining a second utopia value related to local energy generation in the microgrid.

7. The method according to any one of claims 1 to 6, wherein determining the weights includes determining a first weight into which a first objective function relating to energy generation in the grid to which the microgrid is connected is multiplied in the composite objective function, and a second weight into which a second objective function relating to local energy generation in the microgrid is multiplied in the composite objective function.

8. The method according to claim 7, further comprising determining the weights, which in turn involves determining a third weight multiplied by a third objective function related to the energy storage system of the microgrid.

9. The method according to claim 8, wherein the third objective function relates to a battery energy storage system (BESS).

10. The method according to any one of claims 7 to 9, wherein the second objective function includes an emission effect.

11. The method according to any one of claims 1 to 10, wherein determining the utopia value and determining the Pareto front each involve performing optimization under constraints, the constraints including one or more of the power balance, load profile consistency, and / or power generation of the microgrid under a predetermined scenario.

12. The method according to any one of claims 1 to 11, wherein determining the utopia value, determining the Pareto front, and determining the weights are performed for each of a plurality of different scenarios, the different scenarios being distinguished from one another with respect to the load and / or power generation profile of the microgrid.

13. The EMS further includes performing the MOO to determine the one or more asset operating points as a function of time over the forecast period, wherein the objective function in the MOO depends on the determined weights. The method according to any one of claims 1 to 12.

14. The method according to any one of claims 1 to 13, wherein the forecast period includes at least 24 hours.

15. The EMS provides one or more asset operating points to the power management system (PMS), The PMS further includes controlling the controllable assets of the microgrid according to the operating point determined by the EMS, The method according to any one of claims 1 to 14.

16. A system for controlling the operation of an energy management system (EMS), wherein the EMS operates to perform multi-objective optimization (MOO) to determine one or more asset operating points over a forecast period, the MOO determines the one or more asset operating points as a function of time over the forecast period that minimizes a composite objective function which is a weighted sum of several objective functions, and the system, The MOO includes at least one integrated circuit that operates to automatically determine the weights to which the objective function is weighted in the composite objective function used by the EMS, The system includes an interface for providing the determined weights to the EMS, The fact that the at least one integrated circuit automatically determines the weights means that Determining a set of utopian values, wherein each of the utopian values ​​corresponds to one optimal value among several objective functions when optimized independently of other objective functions, Determining the Pareto front of the optimal solution, wherein each of the optimal solutions optimizes the different weighted sums of the several objective functions, Based on the Pareto front and the set of utopian values, the weights to which the objective function is weighted are determined. Includes, The at least one integrated circuit is configured to cluster data acquired for multiple microgrids in order to identify multiple use cases. The system automatically determines the aforementioned weights for at least one of the use cases.

17. It is a microgrid, Multiple controllable assets, An energy management system (EMS) that operates to perform multi-objective optimization (MOO) to determine one or more asset operating points over a forecast period, wherein the MOO determines the one or more asset operating points as a function of time over the forecast period that minimizes a composite objective function which is a weighted sum of several objective functions. A microgrid comprising the system described in claim 16.

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