The invention relates to a device for an optimized operation of a local storage system, e.g. a charge storage in the form of an accumulator but also a thermal storage or a gas storage, in an electrical supply grid connecting distributed generators, for example photovoltaic systems, and distributed loads.
The importance of renewable energy sources is increasing, wherein these energy sources are distributed and are difficult to predict in terms of the amount of energy they can deliver because, by way of example in photovoltaic systems, there is a dependency on the weather. This leads to stability and capacity problems in corresponding electrical power supply grid.
One solution to these problems lies in distributed energy or charge storage devices. Such storage devices are relatively expensive, however, and most be deployed effectively, for example by serving multiple applications from one storage device.
The underlying object of the invention now consists of specifying a device for an optimized operation of a local storage system in an electrical energy supply grid with distributed generators, distributed storage systems and loads such that, taking into account the locally restricted availability of energy and power and the boundary conditions resulting from serving multiple applications from one storage system, such as ensuring the power availability, a cost function for the local storage, e. g. the lifetime of the local storage system within an energy supply grid, is optimized.
This object is achieved in accordance with the invention by the features of claim 1. The further claims relate to preferred embodiments of the invention.
The invention essentially relates to a device for an optimized operation of a local storage system in an electrical energy supply grid with distributed generators, distributed loads and distributed storage devices, in which a storage control unit for the local storage device is present such that, local values are measurable at the local storage and are transmittable to the operator side, locally stored internal installation-dependent control limits are transmittable to an operator side, operational control parameters and/or control limits are receivable from the operator side and a charge/discharge current of the local storage is optimally adjustable at a given point in time by a search of a minimum of a cost function on the basis of the locally-measured values, the locally stored internal installation-dependent control limits and the operational control parameters and/or control limits.
The invention will be explained below on the basis of exemplary embodiments presented in the drawing, in which
The control unit SC is advantageously formed such that the charge/discharge current BF of the local storage S shown in
In a preferable embodiment the cost function, which is to be minimized, corresponds to a service life, which is to be maximized, of the local storage system S and this service life is determinable at least from the usage history h([0, t]) of the storage system and a usage history/storage system service life model for the storage system.
The remaining storage capacity cap_h(t) is determinable from the respective usage history h([0, t]) and a usage history/storage capacity model for the local storage system S. In this case the service life of the local storage system S is attainable if at least the remaining storage capacity cap_h(t) does not fall below a certain critical storage capacity.
The usage history h([0, t]) advantageously comprises accumulated previous charging profiles c(t, (soc_i, soc_f, CC)), wherein these include the number of charging and/or discharging processes of the storage system that have occurred from a specific initial charging state soc_i to a specific end charging state soc_f and with a specific charging current CC after a specific operating period t.
Optionally the remaining storage capacity cap(t) is formable in that a percentage, which is calculated by weighted integration of the accumulated previous charging profiles c(t, (soc_i, soc_f, CC)) over the range of all possible triples comprising initial charging states, end charging states and charging/discharging currents, is subtracted from 100%, wherein the weighting function w also depends on the triples (soc_i, soc_f, CC) and on the storage model.
Optionally an increase in internal resistance r_h(t) is determinable from the usage history h([0, t]) and a usage history/increase in internal resistance model for the storage system S and the service life of the local storage system S is exceeded at least when the local storage system can no longer absorb and/or deliver a specific critical wattage due to the increase in internal resistance.
The operational control parameters and/or control limits p_r, which are to be conveyed using communications technology, for the local storage system S include advantageously at least information about a maximum charging/discharging current limit and/or information about minimum and maximum charging states for local operation and/or maximum charging/discharging current limits for local operation and/or a default value for a charging/discharging current and/or information about electrical grid service requirements.
The measured values p_l(t) locally determined at the local storage system S include advantageously at least one measured time serie or value for voltage frequency and/or voltage and/or spectra of the voltage and therewith the voltage frequency and/or locally generated current and/or locally consumed current and/or local charge state and/or local charging/discharging current and/or electrical grid voltage and/or temperature at at least one location.
The locally stored internal installation-dependent control limits p_f(t) include advantageously at least one maximum charging/discharging current and/or maximum and minimum charging states.
Optionally an optimal charging/discharging current cc(t) can be approximated by a combination of a detailed short-term consideration within a first time interval [t, t+Delta], e.g. by using a simulation, and a long-term consideration within a following second time interval [t+Delta,L], e.g. by using a repetition of part statistics.
The short-term consideration optionally comprises a short-term prediction of expected operational control parameters and/or control limits p_r ([t, t+Delta]) and a variation of the possible charging/discharging current progressions cc([t, t+Delta]) under the condition of the predicted operational control parameters and/or control limits p_r ([t, t+Delta]) and/or the locally stored internal installation-dependent control limits p_f(t).
The variation is advantageously performable by a weighted average of a set of randomly chosen representative progressions of charging/discharging current cc([t, t+Delta]) taking into account the predicted operation control parameters and/or control limits p_f ([t, t+Delta]) and/or the locally stored internal installation-dependent control limits p_f(t) and assigning higher weights to those representative charging/discharging current curves with more favorable cost function values.
The long-term consideration within the second time interval [t+Delta,L] is advantageously performable by a combination of several representative cost functions within a time interval being shorter than the second time interval.
The long-term consideration is also advantageously performable on the basis of results of the short-term consideration.
In the optimization, the following is considered better if two progressions are compared:
In this example, at 14:00, the optimizer searches the optimal state-of-charge curve for the following ten hours. In the optimization, the following is considered better if two progressions are compared:
The optimizer OPT has forecasts for local use and variable generation. Electrical energy supply grid services to be performed are taken into account only by staying within the control limits which are set remotely, since the necessity of grid services is assumed not to be predictable in this case. The result of the optimization is the solid curve. The dashed curves are intermediate steps in the optimization. In a Monte Carlo optimization, these curves would be assigned a significantly lower integration weight than the solid path.
In
Filing Document | Filing Date | Country | Kind | 371c Date |
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PCT/EP2012/064927 | 7/31/2012 | WO | 00 | 6/2/2015 |