Analyzing production and energy requirements of an industrial process

Abstract

An energy requirement analyzing arrangement for analyzing production and energy requirements of an industrial process operated at a production site by a local production system using energy generated by a local energy supply system obtains (S100) energy consumption data and dynamics for the industrial process and the local energy generation at the production site, generates (S110) a prediction of energy generation and a prediction of energy consumption in a time window; estimates (S120) probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption; generates (S130) an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site, determines (S140) an activity for the local energy supply system and / or local production system that reduces the impact on the industrial process and applies (S150) the determined impact reducing activity in the local energy supply system and / or local production system.

Description

[0001] ANALYZING PRODUCTION AND ENERGY REQUIREMENTS OF AN

[0002] INDUSTRIAL PROCESS

[0003] FIELD OF THE INVENTION

[0004] The present invention relates to a method, arrangement, computer program and computer program product for analyzing production and energy requirements of an industrial process operated at at least one production site by a local production system using energy generated by a local energy supply system as well as to an operations system comprising a local production system, local energy supply system and energy requirement analyzing arrangement.

[0005] BACKGROUND

[0006] Industrial operations such as mining, do in many cases require a lot of energy. The operations may additionally be carried out at production sites that are remotely located and where the availability of energy from an energy grid may be limited or even non-existent. Therefore, a local energy supply system is often used to provide the operation with energy.

[0007] Traditionally such local energy supply systems have been based on the use of diesel generators.

[0008] Therefore, it is of interest to use a local energy supply system that comprises renewable energy sources. This has the advantage of reducing the carbon footprint of the mining operation.

[0009] One way in which such a local energy supply system and operations system can be combined in a mining environment is described in CN 111489047. The document seems to be mainly concerned with determining the mix of renewable sources, energy storage resources and backup based on fossil energy. However, there is still a need for improvement in the field.

[0010] SUMMARY OF THE INVENTION

[0011] The invention therefore aims at providing an improvement in the operation of a local production system with energy from a local energy supply system.

[0012] This object is according to a first aspect of the invention achieved through a method of analyzing production and energy requirements of an industrial process operated at at least one production site by a local production system using energy generated by a local energy supply system, the method being performed by an energy requirement analyzing arrangement and comprising:

[0013] - obtaining energy consumption data and dynamics for the industrial process and the local energy generation at the production site;

[0014] - generating a prediction of energy generation and a prediction of energy consumption in a time window;

[0015] - estimating probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption;

[0016] - generating an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site,

[0017] - determining at least one activity for the local energy supply system and / or local production system that reduces the impact on the industrial process, and

[0018] - applying the at least one determined impact reducing activity in the local energy supply system and / or local production system.

[0019] This object is according to a second aspect achieved by an energy requirement analyzing arrangement for analyzing production and energy requirements of an industrial process operated at at least one production site by a local production system using energy generated by a local energy supply system, the energy requirement analyzing arrangement comprising one or more processors with computer program code causing the one or more processors to:

[0020] - obtain energy consumption data and dynamics for the industrial process and the local energy generation at the production site;

[0021] - generate a prediction of energy generation and a prediction of energy consumption in a time window;

[0022] - estimate probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption;

[0023] - generate an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site;

[0024] - determine at least one activity for the local energy supply system and / or local production system that reduces the impact on the industrial process, and

[0025] - apply the at least one determined impact reducing activity in the local energy supply system and / or local production system

[0026] The object is according to a third aspect achieved by a computer program for analyzing production and energy requirements of an industrial process operated at at least one production site by a local production system using energy generated by a local energy supply system, the computer program comprising computer program code causing, when being loaded into one or more processors of an energy requirement analyzing arrangement, said one or more processors to:

[0027] - obtain energy consumption data and dynamics for the industrial process and the local energy generation at the production site;

[0028] - generate a prediction of energy generation and a prediction of energy consumption in a time window; - estimate probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption;

[0029] - generate an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site;

[0030] - determine at least one activity for the local energy supply system and / or local production system that reduces the impact on the industrial process; and

[0031] - apply the at least one determined impact reducing activity in the local energy supply system and / or local production system.

[0032] The object is according to a fourth aspect achieved by a computer program product for analyzing production and energy requirements of an industrial process operated at at least one production site by a local production system using energy generated by a local energy supply system, the computer program comprising a computer-readable storage medium comprising the computer program code of the third aspect.

[0033] The object is according to a fifth aspect achieved by an operations system comprising a local production system operating an industrial process at at least one production site, a local energy supply system generating energy for the local production system and an energy requirement analyzing arrangement, the energy requirement analyzing arrangement being operative to:

[0034] - obtain energy consumption data and dynamics for the industrial process and the local energy generation at the production site;

[0035] - generate a prediction of energy generation and a prediction of energy consumption in a time window;

[0036] - estimate probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption; - generate an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site;

[0037] - determine at least one activity for the local energy supply system and / or local production system that reduces the impact on the industrial process; and

[0038] - apply the at least one determined impact reducing activity in the local energy supply system and / or local production system.

[0039] The at least one or more processors of the energy requirement analyzing arrangement may implement a digital environment module representing the industrial process, a simulation module and an impact mitigation module, where the digital environment module is operative to receive the energy consumption data and dynamics and generate the prediction of energy supply and energy consumption, the simulation module is operative to receive the predictions of energy supply and energy consumption, estimate the probabilities of energy surplus and energy shortage and generate the impact evaluation and the impact mitigation module is operative to receive the impact evaluation and determine the at least one activity.

[0040] The production system may be a mining system and the at least one production site maybe a mining site.

[0041] The determining of at least one impact reducing activity may comprise determining a number of activities for the local energy supply system and / or local production system that reduce the impact on the industrial process.

[0042] In a variation of the first aspect, the method may then further comprise analyzing the effect of the determined impact reducing activities, and the applying of at least one determined impact reducing activity may comprise applying one or more of the determined impact reducing activities based on the analysis.

[0043] In a corresponding variation of the second aspect, the energy requirement analyzing arrangement, and with advantage the impact mitigation module thereof, may be further operative to analyze the effect of the determined impact reducing activities, and the applying of at least one determined impact reducing activity may comprise applying one or more of the determined impact reducing activities based on the analysis.

[0044] The analysis may comprise analyzing the activities with regard to one or more analyzing criteria, for instance one or more Key Performance Indicators (KPI), such as sustainability, production output, production robustness or cost. The activities may additionally be optimized with regard to at least one analyzing criterion.

[0045] The applying of the at least one determined impact reducing activity may comprise instructing the local energy supply system and / or local production system to perform the at least one impact reducing activity .

[0046] The local energy supply system may comprise renewable energy sources, such as wind and / or solar energy sources like wind and / or photovoltaic generators. In this case the energy generated by the local energy supply system may comprise renewable energy.

[0047] In case the local energy supply system comprises renewable energy sources, the prediction of energy generation may additionally be made based on weather conditions in the time window. If the renewable energy sources comprise wind generators, the weather conditions may comprise wind conditions. If the renewable energy sources comprise photovoltaic generators, the weather conditions may comprise solar conditions. The renewable energy sources may additionally or instead comprise geothermal energy sources, in which geothermal conditions at the production site may be considered.

[0048] The energy supply system may additionally comprise a mixture of energy storage elements and renewable energy sources.

[0049] Furthermore, the weather conditions may comprise historic weather conditions at the production site corresponding to the time window, such as historic wind data and / or historic solar data in one or more historic time intervals corresponding to the time window, such as in previous years if the time window is a present year or the same month during previous years if the time window is a month of a present year.

[0050] When the weather conditions comprise historic weather conditions, the at least one determined impact reducing activity may comprise changing the mixture of used energy storage elements and renewable energy sources. The changing of the mixture of used energy storage elements and renewable energy sources may comprise changing generators and / or energy storage elements from standby to active operation or vice versa.

[0051] The weather conditions may also comprise weather forecasts at the production site for the time window, such as wind and / or sun forecasts.

[0052] When the weather conditions comprise weather forecasts, the at least one impact reducing activity may comprises a change of the operation in the production system and / or operation of the energy supply system. A change of operation in the production system may comprise a change of the production in the production system, such as an increase in production efforts when there is an energy surplus or a decrease in production efforts when there is an energy shortage. The increase in production efforts may comprise a stockpiling of production results or intermediate production results. The production system may comprise a main production part and an auxiliary part. A change of operation of the production system may additionally comprise a change in the auxiliary part.

[0053] A change of operation of the energy supply system may additionally comprise storing at least some of the energy surplus in one or more energy storage elements when there is an energy surplus and compensating at least some of the energy shortage with energy from the one or more energy storage elements when there is an energy shortage.

[0054] BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will in the following be described with reference being made to the accompanying drawings, where

[0056] Fig. 1 schematically shows a production system in the form of a mining system,

[0057] Fig. 2 schematically shows an energy supply system for powering the mining system,

[0058] Fig. 3 schematically shows a first realization of an energy requirement analyzing arrangement for analyzing production and energy requirements of an industrial process operated by the production system using energy generated by the energy supply system,

[0059] Fig. 4 schematically shows a second realization of the energy requirement analyzing arrangement,

[0060] Fig. 5 schematically shows a computer-readable storage medium with computer program code, in the form of a CD-ROM disc, for implementing the energy requirement analyzing arrangement,

[0061] Fig. 6 shows a flow chart of number of method steps performed by the energy requirement analyzing arrangement in a method of analyzing production and energy requirements of an industrial process operated by the production system using energy generated by the energy supply system, Fig. 7 shows a relationship between a probability of exceedance and an average annual energy production,

[0062] Fig. 8 shows the probability of generating k kW of energy in a time window of a year,

[0063] Fig. 9 shows a load together with renewable generation of energy in a part of the time window as well a renewable energy over supply or deficit in the part of the time window.

[0064] Fig. io schematically shows the energy requirement analyzing arrangement when used for designing an operations system comprising the production system and the energy supply system, and

[0065] Fig. n schematically shows the energy requirement analyzing arrangement when used with an operations system that is in use.

[0066] DETAILED DESCRIPTION OF THE INVENTION

[0067] In the following there will be given a detailed description of embodiments of an operations system and a method, energy requirement analyzing arrangement, computer program and computer program product for analyzing production and energy requirements of an industrial process operated at a production site by a local production system using energy generated by a local energy supply system.

[0068] Fig. 1 schematically shows a production system PS io, which as an example is a mining system. The production system io is a local production system provided at a production site, which site may thus be a mining site.

[0069] The production system io comprises a number of operations. As an example, there is a first ore handling operation 0H01, which maybe a first ore processing operation that as an example is a blasting operation. This is followed by a second ore handling operation 0H02, which may be a first ore transporting operation involving the transporting of ore using equipment such as Load Haul and Dump (LHD) vehicles 16. The second ore handling operation OHO2 is followed by a first intermediate ore storing operation IOSO1, which first intermediate storing operation IOSO1 may involve storing of ore in one or more ore passes of a first intermediate ore storage system 18. After the first intermediate ore storing operation IOSO1 follows a third ore handling operation OHO3, which may be a second ore transporting operation involving a transporting by ore transporting equipment 20 such as trucks, trains, wagons or conveyor belts. Thereafter follows a second intermediate ore storing operation IOSO2, which may likewise be carried out using ore passes in a second intermediate ore storage system 22. After the second intermediate ore storing operation IOSO2 follows a fourth ore handling operation OHO4, which maybe a second ore processing operation such as an ore crushing operation using equipment such as one or more ore crushers 24. Thereafter may follow a third intermediate ore storing operation IOSO3, which maybe carried out using ore passes in a third intermediate ore storage system 26. The third intermediate ore storing operation IOSO3 is finally followed by a fifth ore handling operation OHO5, which maybe a third ore transporting operation that as an example is a hoisting operation using hoisting equipment 28. A hoisting operation may also be considered to be an ore handling operation.

[0070] It should here be realized that the system in fig. 1 is a mere example and that further or fewer operations may be included. It is also possible with additional types of operations, such as charging, drilling and comminution. Finally, it should also be realized that a mining system is merely one type of production system. Other examples are chemical process systems and manufacturing systems. The operations are handled by a main production part of the production system.

[0071] Furthermore, there may additionally exist one or more auxiliary parts associated with the production. In the figure one such auxiliary part in the form of a dewatering system DW 30 is shown at the third ore handling operation OHO3. The dewatering system 30 provides dewatering at one or more of the shown operations. Another auxiliary part is ventilation. In order to control and / or plan at least some of the operations there is a process system controller PSC 32 controlling or providing operation instructions to equipment used in the first ore handling operation OHO1, the second ore handling operation OHO2, the third ore handling operation OHO3, the fourth ore handling operation OHO4, the fifth ore handling operation OHO5, and the dewatering system 30. Also equipment used in the first, second and third intermediate or operations IOSO1, IOSO2, IOSO3 maybe controlled or provided with operating instructions.

[0072] The production system 10 needs energy in order to operate properly. For this reason there maybe provided an energy supply system, which is a local energy supply system at the production site.

[0073] Fig. 2 shows one example of the local energy supply system ESS 34 for the production system at the production site.

[0074] The energy supply system 34 comprises a bus 52 and a number of subsystems connected to the bus 52. There is first subsystem 36 connected to the bus 52 via first converter 38, which first subsystem 36 comprises fluid-operated generators FOG, such as diesel or gas generators. These generators may be based on diesel engines or gas turbines. There is also a second subsystem 40 connected to the bus 52 via a second converter 42, which second subsystem is a photovoltaic or solar energy SE subsystem comprising photovoltaic generators. There is a third subsystem 44 connected to the bus 52 via third converter 46, which third subsystem is a wind energy WE subsystem comprising wind turbines. Finally, there is a fourth subsystem 48 connected to the bus 52 via a fourth converter 50, which fourth subsystem 48 is an energy storage ES subsystem, comprising energy storage elements such as batteries and / or flywheels. It can thereby be seen that the energy supply system 34 comprises renewable energy sources, such as wind and / or solar energy sources like wind and / or photovoltaic generators. As can also be seen, the energy supply system 34 may comprise a mixture of energy storage elements and renewable energy sources.

[0075] The renewable energy sources may additionally comprise geothermal energy sources (not shown).

[0076] The bus 52 is in the example of fig. 1 an Alternating Current (AC) bus. The first and third subsystems 36, 44 typically operate with AC and for this reason the first and third converters 38, 46 are AC / AC converters. On the other hand, the second and fourth subsystems 40, 48 typically operate with direct current (DC) and for this reason the second and fourth converters 42, 50 are AC / DC converters. However, it is possible that the bus is a Direct Current (DC) bus, in which case the first and third converters 38, 46 may instead be AC / DC converters, while the second and fourth converters 42, 50 may instead be DC / DC converters. The bus 52 may also be connected to a grid 56, such as a utility grid. This is indicated in fig. 1 through the bus 36 being connected to the grid 56 via a further converter 54, which in this case is also an AC / AC converter.

[0077] An energy supply system controller ESSC 58 is also provided for controlling or planning the operation of the energy supply system 34, which maybe done through controlling the subsystems 36, 40, 44, 48 and converters 38, 42, 46, 50.

[0078] Aspects of the present disclosure are concerned with analyzing production and energy requirements of an industrial process operated at the production site by the production system using energy generated by the energy supply system.

[0079] For this reason there is provided an energy requirement analyzing arrangement. Fig. 3 shows a first realization of the energy requirement analyzing arrangement ERAA 60. The energy requirement analyzing arrangement 60 comprises one or more processors with computer program code causing the one or more processors to implement an energy requirement analyzing function. In the present example, the energy requirement analyzing arrangement 60 comprises one processor PR 62 and one memory M 64 or data storage with computer program instructions CI 66 that, when executed by the processor 62, implements the energy requirement analyzing function.

[0080] The energy requirement analyzing arrangement 60 may thus comprise a processor 62 with associated program memory 64 including computer program code or computer program instructions 66 for implementing the energy requirement analyzing function.

[0081] Fig. 4 shows a second realization of the energy requirement analyzing arrangement ERAA 60. It maybe realized as a digital environment module DEM 68, a simulation module SM 70 and an impact mitigation module IMM 72, which modules may in a similar way be realized using one or more processors and one or more memories with computer program code. The energy requirement analyzing arrangement 60 may thus be provided through one or more processors implementing the digital environment module 68, the simulation module 70 and the impact mitigation module 72.

[0082] The computer program code used to implement the energy requirement analyzing arrangement may also be provided via a computer program product, for instance in the form of a computer readable storage medium or data carrier, like a CD ROM or a memory stick, carrying a computer program with the computer program code, which will implement the energy requirement analyzing function when being loaded into one or more processor of the energy requirement analyzing arrangement. One such computer program product in the form of a CD ROM 74 with the above-mentioned computer program code 66 is schematically shown in fig.

[0083] 5-

[0084] The production system, the energy supply system and energy requirement analyzing arrangement may all be parts of an operations system.

[0085] The use of renewable energy generators (i.e., solar photovoltaics and wind turbines) in the energy mix has become increasingly popular due to their modularity, environmental-friendliness, and natural availability. However, the major drawbacks of renewable energy generators are their temporal intermittency, and seasonal and long-term variability. Hence, these generators pose resource reliability risk that can be manifested as a quantity risk— i.e., the risk that the quantity of wind and sunshine will be less than expected. The resource reliability risk imposes uncertainty in short-, medium- & long-term energy generation and consumption. Hence such risk needs to be actively considered and mitigated during evaluation and operational phase of an energy supply system that comprises renewable energy sources.

[0086] Aspects of the present disclosure are directed towards quantifying and predicting the energy supply risk in a production system being supplied with power by such an energy supply system. Subsequently, computation of the effect of risk mitigation options are made, e. g. the optimal size of energy storage and / or backup / emergency energy generator or through grid or through demand flexibility to mitigate the energy supply risk with predefined objectives i.e., minimum cost, minimum emission, etc.

[0087] The economic value of renewable energy generators depends on the availability of renewable resources and the ability to consume energy during production. Hence, the resource assessment is a crucial part of a feasibility study for operations systems with energy supply systems employing renewable energy sources. Even though the approaches for resource assessment typically vary depending on many factors like purpose, stage of development, generator types under consideration i.e., wind or solar or hybrid, etc., such calculations are often based on some onsite measurement of the resources, sometimes augmented by satellite data and meteorological modeling, and likely to be combined with longer-term measurements from offsite (but ideally nearby) reference stations. Most often a so-called “P50” estimate of wind speed or solar irradiance is used to calculate the annual energy production that forms the basis for economic calculation. This introduces two primary sources of potential error or bias: 1) the systematic bias from the resource measurement and / or modeling techniques used and 2) the random error related to the inherent short-, medium- and long-term variability of the resource over time. There is a third error of systemic type from energy converter models that are used to estimate amount of energy generation.

[0088] Another aspect that is often overlooked in such traditional approaches to the feasibility study is that during the operational phase the energy demand must always be matched by the energy available instead of ensuring only annual balance. Meaning energy must be balanced at shortterms like 15-minutes, hourly, etc. and at medium-terms like daily, weekly, monthly, etc. to long-term like yearly and over the project lifetime. Thus, the traditional methods overlook the dynamic energy supply risk and are unable to analyze and provide risk mitigation options and their associated costs. In addition to this, for completeness, such a feasibility study should also consider options related to the other side of the energy balancing act i.e., the demand side flexibility options. Energy consumption peaks should be avoided to reduce the risk of emergency shutdowns and high peak price payments. At least, the decision makers need ways / tools to compare different risk mitigation alternatives related to both supply- and demandside that also includes associated costs of corresponding options. For example, what are the overall costs and benefits of reducing peak energy demand at rear times with no or exceptionally low renewable generation against installing additional energy storage or emergency backup generators to cover rear peaks. Thus, there is a need for a well-defined method / tool to quantify, predict and reduce the operational risks of energy supply and to evaluate ways to reduce these risks. The need for quantifying, predicting and reducing operations risks are also needed when the operations system is operating.

[0089] One idea is to calculate, predict, and reduce operational risks and / or increase operational opportunities of energy supply. Additionally, to evaluate ways to reduce these risks using the simulation capability of a digital environment module 68 or eMine Digital Twin. To do so, the energy supply risk in a hybrid or renewable energy generation system is quantified and predicted. Several approaches for renewable resource risk quantification i.e., probability of exceedance, reliability (% of load met by the renewables), probability of generating at least x kW etc. are utilized for this purpose. Subsequently, optimal size of energy storage and / or backup / emergency energy generators or need to buy and / or sell from grid are calculated, as well as different demand flexibility measures are analyzed and proposed to mitigate the energy supply risk with predefined objectives i.e., minimum cost, minimum emission, etc.

[0090] How this may be done, will now be described with reference also being made to fig. 6, which shows a flow chart of a number of method steps performed by the energy requirement analyzing arrangement in a method of analyzing production and energy requirements of an industrial process operated by the production system io using energy generated by the energy supply system 34.

[0091] The goal is to simulate, quantify and mitigate the risk of a lack of energy or surplus energy or to make use of the opportunity of energy lack or surplus of a real site in a digital environment of the real site. Therefore, aspects of the present disclosure may play a decisive part in the electrification of mines from the first concept to daily use. The process can be broadly separated into three steps: 1. Simulate and predict energy production, consumption, transmission and storing

[0092] 2. Quantify energy-related risks and opportunities

[0093] 3. Optimize mitigation activities with respect to e.g., cost or emissions

[0094] The operation may start through obtaining, by the digital environment module 68, energy consumption data and dynamics for the industrial process and the local energy generation at the production site, S100, and generating a prediction of energy generation and a prediction of energy consumption in a time window, S110. As the local energy supply system may comprise renewable energy sources, the energy generated by the local energy supply system may comprise renewable energy. Because of this, the prediction of energy generation may also be a prediction of renewable energy generation. In case the local energy supply system comprises renewable energy sources, the prediction of energy generation may additionally be made based on weather conditions in the time window. If the renewable energy sources comprise wind generators, the weather conditions may comprise wind conditions. If the renewable energy sources comprise photovoltaic generators, the weather conditions may comprise solar conditions. If the renewable energy sources comprise geothermal energy sources, then geothermal conditions at the production site may be considered. Furthermore, the weather conditions may comprise historic weather conditions corresponding to the time window, such as historic wind data and / or historic solar data at the mining site in one or more historic time intervals corresponding to the time window, such as in previous years if the time window is a present year or the same month during previous years if the time window is a month of a present year. The weather conditions may also or instead comprise weather forecasts for the time window at the mining site, such as wind and / or sun forecasts. The prediction of energy generation and the prediction of energy consumption may be obtained using energy consumption and production simulations made in the digital environment module 68, the so-called digital twin. The digital twin is a software collection of models, simulations, services, algorithms and their respective data input and output, which is programmed on a computer, server, or cloud by experts. These elements represent all relevant energy consumers and producers of the site and their interplay and are used to predict the energy production and consumption for a specific time horizon. For an accurate representation of all energy production and consumption each process may need to be modeled sufficiently precisely. For example, for the dewatering 30 at the site, the model of this process must include the number of pumps, their respective power and pumping dynamics, their location, the height difference over which water is pumped, composition of pipes, different pumping modes and more to account for an accurate model of dewatering. For a specific time-horizon, for example a week, this model, which is composed in essence of mathematical expressions, is then used to calculate the predicted energy load for dewatering in a specific granularity, for example in a minute sharp timeseries. Models may need to be validated, for example by comparison with benchmark models or already existing real processes to guarantee accurate results. Models may in the same way be used for the other operations, such as the blasting OHOi, crushing OHO4 and various transporting operations OHO2, OHO3, OHO5.

[0095] Models can come from many different sources and be built using different methodologies:

[0096] Simulation Models can be built by Simulation Experts in specialized simulation tools. These models can for example be exported using FMUs (Functional Mockup Units) or as C functions

[0097] Predictions Models can be built by Machine Learning experts and exported as ONNX (Open Neural Network Exchange) files or as C++ functions Time series data models can be extracted by process engineers from reference plants and stored as csv files or in a time series database.

[0098] A digital twin is a real-time virtual representation of a real-world physical system or process (a physical twin) that serves as the indistinguishable digital counterpart of it for practical purposes, such as system simulation, integration, testing, monitoring, and maintenance. Like the real process, a Digital Twin provides a meta-model process description of the process. The meta- model process description can be provided using AutomationML, Asset Administration Shell, or OPC UA models.

[0099] The overall simulation model for the energy consumption, which comprises sub-models for each process, takes as an input the energy consumption data and energy consumption dynamic, i.e., how fast a process starts up or slows down or how fast it reacts to changes, for all relevant processes present at the site, i.e., processes that consume a measurable and for the operator significant amount of energy. Energy consumption input data and their dynamics may need to be measured at the site or calculated / predictedby experts beforehand. Sub-models may include heating and cooling systems, ventilation of clean and dirty air, chemical processes to clean or alter material, comminution and crushing processes, smelting processes, manufacturing processes, transportation systems such as truck hauling, conveyor belt systems and hoists, auxiliary systems e.g., lighting and safety systems, operations and management systems, water systems, drilling and blasting processes, cement production, paper, and pulp production, etc. The data can stem from already existing processes or systems, such as previously existing process software or can be simulated and extrapolated.

[0100] Like the real mining system, the simulation model modules can be changed and refined over time depending on the use case. The overall simulation model for the energy production, which again may be formed using sub-models, takes as an input the energy production data and energy production characteristic of already existing and potentially in future installed or standby energy producing generators of the energy producing subsystems 36, 40, 44 of the energy supply system 34. This includes power and power characteristics of conventional energy generators such as combined heat and power systems, diesel generators, gas, or steam turbines, hydropower plants, energy storages of all kinds, such as battery storages or mechanical / electrical storage systems. Furthermore, it takes as input power characteristics of existing or future renewable energy producers, such as photovoltaic or wind generators as well as site specific weather conditions that may comprise wind conditions and / or solar conditions. The weather conditions may also or instead comprise weather forecasts for the time window, such as wind and / or sun forecasts. Thus, the weather conditions may comprise weather data, historic or predictive. In case the local energy supply system comprises renewable energy sources, the prediction of energy generation may additionally be made based on weather conditions in the time window.

[0101] This input data stems from already existing process software or is calculated by experts based on the technical characteristics of a process. For example, if there will be a solar power generation unit present at the site, based on characteristics of the specific unit such as maximum power output and size as well as using weather record data, i.e., solar radiation at the site, the model for solar can predict the produced energy for example for a year with the granularity of for example 15-minute blocks.

[0102] The output of the models are predictions for the energy consumption and production for a specific time horizon or time window. It is possible that the only energy sources considered at this stage are the renewable energy sources and that therefore the energy production that is to be considered is the energy production using renewable energy sources. The time horizon can cover minutes, hours, days, weeks, months, years. The output data may be presented in timeseries format for the chosen time horizon in specific granularity.

[0103] The predictions are then provided to the simulation module SM 70, which goes on and estimates probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption, S120, as well as generates an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site, S130. Thereby the energy shortage and energy surplus may the energy shortage or energy surplus of the renewable energy sources.

[0104] The simulation module 70 thus quantifies the probability of a deviation of energy supply and demand during a certain time horizon or time window. This takes as an input the energy production and consumption data from the digital environment module 68. Then Monte Carlo simulations, or what-if simulations may be run using the time series or other data from the digital environment module 68 to generate probability distributions for a certain energy surplus or lack during a timeframe. While Monte Carlo simulation performs random sampling of input variables and conducts a large number of simulations, which displays the statistical characteristics of the model outputs to their distributions, a what-if analysis on the other hand is a technique to determine how predictions are affected by the changes in the assumptions by considering different scenarios. As an output a probability distribution will be generated that depicts the delta / gap between energy supply and demand and its respective probability during a certain time horizon.

[0105] There are multiple approaches available to quantify the resource risk. One of the most widely used is probability of exceedance as illustrated in fig. 7, which, with just a few key inputs, can be used to estimate the probability that, for example, the wind or solar resource at a given site will fall below a given level. Fig. 7 shows the probability of exceedance (POE) as a function of the average annual energy production (AEP) of the site, where the energy production as an example ranges between 60 and 140 GWh. The function is shown as a curve 76.

[0106] POE allows a comparison of the resource risk among multiple project sites in terms of probabilistic values. Probability of exceedance is often represented as “P-levels” ranging from Pi (i.e., there is only a 1% chance that actual generation will exceed the Pi estimate) to P99 (i.e., there is a 99% chance that actual generation will exceed the P99 estimate). In comparison to the central or median estimate that is equivalent to P50 estimate, the probability of exceedance allows the project analyst to choose different “P-levels” for wind and / or solar generation. In the example of fig. 7 a P50 level 78 is shown. From the P50 level 78 it can be seen that there is a fifty percent chance of the renewable energy production exceeding the load when the load is 97.5 GWh.

[0107] Other common ways to quantify the renewable resource risk are- 1) calculating the reliability (i.e., the percentage of demand met by renewables) and 2) calculating probability of generating at least k kW of renewable power.

[0108] The latter approach is exemplified in fig. 8, which shows the probability of generating at least k kW in a time window TW of 12 months, when k is 5 kW, 6 kW and 7 kW. There is thus a first curve 80 showing the probability of generating at least 5 kW, a second curve 82 showing the probability of generating at least 6 kW and a third curve 84 showing the probability of generating at least 7 kW in the time window TW, which time window is twelve months.

[0109] Additionally, simple indicators like energy deficit index and energy over supply index can be calculated to quantify energy supply risk. Energy deficit index may be defined as the ratio between energy shortage and energy demand in a particular hour. Similarly, the energy over supply index may be the ratio between energy oversupply and energy demand in a particular hour. This is exemplified in fig. 10.

[0110] Fig. 9 shows a probability curve 86 for renewable energy generation and a probability curve 88 for the load during a part of the previously mentioned time window TW, which part stretches from o:oo at the beginning of a day to o: oo at the beginning of a following day. There are also areas of over supply and areas of energy deficit. There is a first area of over supply OSi between o:oo and 5:00, a first area energy deficit EDi between 06:00 and 09:00, a second area of oversupply OS2 between 09:00 and 14:00 and a second area of energy deficit ED2 between 14:00 and 23:00. Furthermore, a load Lti at first time ti and an energy over supply OSti at the first time are shown in relation to the second area of oversupply OS2, while a load Lt2at second time t2 and an energy deficit EDt2at the second time t2 are shown in relation to the second area of energy deficit ED2, where the first time ti is 12:00 and the second time t2 is 20:00.

[0111] As there is an energy over supply at the first point time ti, the reliability of oversupply of renewable energy is the load divided by the energy overs supply at the first point in time, i.e. Lti / EDti. However, the reliability of the renewable energy production at the second point in time t2 is the energy deficit divided by the load at the second point in time, i.e. EDt2 / Lt2.

[0112] The probabilities of energy surplus or energy shortage at the different points in time are then provided to the impact mitigation module 72, which goes on an generates an impact evaluation that quantifies the impact of the probability of energy surplus and energy shortage at the production site, S130, determines at least one activity for the local energy supply system and / or local production system that reduces or mitigates the impact on the industrial process, S140, and applies the determined impact reducing or mitigating activity in the local energy supply system 34 and / or local production system 10, S150.

[0113] The determining of at least one impact reducing activity may comprise determining a number of activities for the local energy supply system and / or local production system that reduce the impact on the industrial process. In this case the impact mitigation module may additionally analyze the effect of the determined impact reducing activities and apply one or more of the determined impact reducing activities based on the analysis.

[0114] The analyzing may comprise analyzing the activities with regard to one or more analyzing criteria, for instance one or more Key Performance Indicators (KPI), such as sustainability, production output, production robustness or cost. The activities may additionally be optimized with regard to at least one analyzing criterion,

[0115] When the weather conditions comprise historic weather conditions, the mitigating activity may comprise changing the mixture of used energy storage elements and renewable energy sources. The changing of the mixture of used energy storage elements and renewable energy sources may comprise changing generators and / or energy storage elements from standby to active operation or vice versa. When the weather conditions comprise weather forecasts, the mitigating activity may comprise a change of the production and / or operation of the energy supply system. A change of the production may comprise an increase in production efforts when there is an energy surplus or a decrease in production efforts when there is an energy shortage. A change of operation of the energy supply system may additionally comprise storing at least some of the energy surplus in one or more energy storage elements when there is an energy surplus and compensating at least some of the energy shortage with energy from the one or more energy storage elements when there is an energy shortage. In the impact mitigation module 72, possible impacts of energy risks or opportunities are quantified, different mitigation and response activities are evaluated for beneficial impact such as sustainability and costs and possibly also optimized, an energy and operations management system informed about the subsequent, possibly optimized, activities in the form of a software exchange, which energy and operations management system may be formed by the process system controller 32 and the energy supply system controller 58. The probability distributions for energy risks and opportunities are analyzed to quantify their impact on the production or processes at the site.

[0116] For example, how probable and how severe an energy surplus or lack of energy will be regarding equipment failure, production stops, accidents, short circuits, energy costs, etc. during a specific time horizon. The impact mitigation module 72 then takes the resulting impact evaluation and may optimize different mitigating activities to alleviate and prevent possible negative impacts or to make use of energy opportunities. These measures, which may be curated and added to the impact mitigation module 72 by experts beforehand, include for example demand side management of processes and production, i.e., the short-term increase or decrease of flexible, energy-consuming processes, storage optimization of for example batteries, electrical, heat or mechanical storages, rescheduling of production, long-term planning or buying or selling energy to account for surplus or shortage. These measures are different in their time to use and in the volume with which they can serve as mitigation activities. For example, if the impact mitigation module 72 predicts a high probability of energy shortage during the next 8 hours, demand side management can be used to mitigate these effects by for example turning off or lowering the performance of auxiliary part of the production system like dewatering 30 or of the main production facilities OHO1, OHO4 and OHO5 themselves for said hours. Additionally, the impact mitigation module 72 could recommend buying more energy at short notice at intraday markets. On the other hand, if the impact mitigation module 72 predicts an oversupply of energy during the next 8 hours, a recommended activity could be to sell the additional energy, participate in grid stability response markets, increase production efforts to for example stockpile intermediate products or material, such as in stores 18, 22 and / or 26, (time shifting of production) or to feed surplus energy into the energy storage system 48. If for example the impact mitigation module 72 predicts high energy volatility during the next 2 years, it does make sense to increase storage unit capacity and as a result be able to mitigate energy volatility using that storage. If the impact mitigation module 72 predicts long-term energy shortages during the next 2 years an optimal recommendation could be to invest in extra renewable energy capacity. If it predicts an energy surplus, it might recommend adding additional production facilities. The optimization can be done with respect to different KPIs (Key Performance Indicators) like costs, production output, production robustness or a sustainability KPI like emissions. Different optimization procedures are at disposal to account for variations at sites. In general, however, for each point in time the energy that is consumed must match the energy that is produced. This constraint must be valid independently of the chosen optimization technique.

[0117] The optimal risk-reducing mitigation activities may then be fed back into the operations and energy management system, also incorporated in the digital twin. The connection between the energy management system and the energy risk probabilities, the optimized mitigation measures as well as the electrical grid information maybe made possible e.g., by REST API and is routed via an Edge device or a cloud service. Timeseries data may be stored in a database. Individual models maybe containerized and maybe also hosted via Edge device / cloud. The activities may additionally be implemented in the energy supply system and / or the production system. The impact mitigation module 72 or the digital environment module 68 may thus inform the local energy supply system and / or local production system about the mitigating activity for influencing the system in question, which maybe done through informing the production system controller 32 and / or the energy supply system controller 58 of the mitigating activity.

[0118] The controller in question may then control the corresponding production or energy supply system to implement the mitigating activity.

[0119] This principle can be applied to production, service, or process sites in many different industries like mining, chemical process industries, manufacturing, retail, residential buildings, construction etc.

[0120] Figure 10 illustrates an example application during the design phase of the operations system.

[0121] Process Steps:

[0122] 1. Prerequisite: A Base Case scenario 128 provides information about the selected optimization criteria 130, an energy production curve 132 and an energy consumption curve 134.

[0123] 2. Prerequisite: A Mitigation Activity Library 90 provides appropriate structural, operational, strategic options 92, 94, 96 to address the risks opposed by the base case scenario 128. The Mitigation Activity Library 90 maybe pre-populated by experts but can be extended later. Structural Options 92 are e.g., add batteries, decrease amount of PV. Operational Options 94 are e.g., adjust production to energy production. Strategic Options 96 are e.g., long-term agreements with energy provider to buy / sell energy or participation in a demand side management program

[0124] 3. Prerequisite: Simulation and / or process experts may need to provide models to populate the energy production model library 116 and the energy consumption model library 122. These models can include PV models 118, 124 and Battery Models 120, 126 and may additionally be the same ones which were used to generate the base case scenario

[0125] 4. Prerequisites: all information and modules depicted in fig. 10 may be available via the cloud to users world-wide. 5. A User selects parameters 142 using a web interface 136. Parameters 142 can be a sub-set of applicable mitigation activities, optimization time horizon etc.

[0126] 6. The optimization module populates a meta model 108 in a mine metamodel configuration file 106 using the provided inputs and information from the previous steps

[0127] 7. If a what-if analysis 114 is requested, a simulation module no executes for each parametrized scenario the same simulation as for the generation of the base case scenario taking advantage of the Monte-Carlo simulation module 112.

[0128] 8. If Mining System 100 like Energy Management 102 and / or Planning and Scheduling 104 are connected, these systems can also be used for a real optimization of energy consumption and production using the provided mitigation activities. If no Mining systems 100 are connected a simplified optimization can be used instead.

[0129] 9. The results of optimization / simulation are stored in the meta model 108.

[0130] 10. The results are compared with the base case 128 and presented, such as visualized 138, via the web interface 136 to the user. Also, a report 140 can be downloaded.

[0131] 11. Alternatively, at least some of the results are, perhaps after acceptance by an operator, automatically applied in the operations system

[0132] Assuming the operational setup has been identified and the mine is up and running, fig. 11 illustrates an example application during the operational phase.

[0133] Process Steps:

[0134] 1. Prerequisite: Mining systems like Energy Management 144 and Planning and Scheduling 148 provide information about the energy production curve 132 and the energy consumption curve 134. The user may also provide information about optimization criteria 130. 2. Prerequisite: The Mitigation Activity Library 90 provides appropriate structural, operational, strategic options 92, 94, 96 to address the risks opposed by the base case scenario 128. The Mitigation Activity Library 90 maybe pre-populated by experts but can be extended later. Structural Options 92 are e.g., add batteries, decrease amount of PV. Operational Options 94 are e.g., adjust production to energy production. Strategic Options 96 are e.g., long-term agreements with energy provider to buy / sell energy or participation in a demand side management program

[0135] 3. Prerequisite: Simulation and / or process experts may need to provide models to populate the energy production model library 116 and the energy consumption model library 122. These models can include PV models 118, 124 and Battery Models 120, 126 and may additionally be the same as those which were used to generate the base case scenario 128. For application during the operational phase, these models may also need to take simulation run time aspects into account.

[0136] 4. Prerequisites: all information and modules depicted in Figure 11 may be available to local users via an edge device which is connected to a Mine Operational systems like Energy Management 144 and Planning and Scheduling 146. These systems provide plans about planned energy consumption curves and planned energy production curves for each point in time.

[0137] 5. The User selects parameters 142 via a web interface 136. The parameters 142 can be a sub-set of applicable mitigation activities, optimization time horizon, etc.

[0138] 6. The optimization module 98 populates a meta model 108 in a mine metamodel configuration file 106 using the provided inputs and information from the previous steps

[0139] 7. If a what-if analysis 114 is requested, a simulation module no executes for each parametrized scenario the same simulation as for the design phase scenario taking advantage of the Monte-Carlo simulation 112 module.

[0140] 8. If Mining System like Energy Management 102 and / or Planning and Scheduling 104 are connected (another instance, not the same as those that provide plans about planned energy consumption curves and planned energy production curves), these systems can also be used for a real optimization of energy consumption and production using the provided mitigation activities. If no Mining systems 100 are connected a simplified optimization can be used instead. The use of two Energy Management and Planning and Scheduling instances allows to separate the execution for the real mining process and the execution for the parametrized virtual e Mine Digital Twin Mining System.

[0141] 9. The results of optimization / simulation are stored in the meta model 108.

[0142] 10. The results are compared with the current case and presented, such as visualized 138, via the web interface 136 to the user. Also, a report 140 can be downloaded or the results to be exposed via an API to other mining systems. 11. Alternatively at least some of the results are, perhaps after acceptance by an operator, automatically applied in the operations system.

Claims

CLAIMS1. A method of analyzing production and energy requirements of an industrial process operated at at least one production site by a local production system (10) using energy generated by a local energy supply system (34), the method being performed by an energy requirement analyzing arrangement (60) and comprising:- obtaining (S100) energy consumption data and dynamics for the industrial process and the local energy generation at the production site;- generating (S110) a prediction of energy generation and a prediction of energy consumption in a time window;- estimating (S120) probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption;- generating (S130) an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site;- determining (S140) at least one activity for the local energy supply system and / or local production system that reduces the impact on the industrial process; and- applying (S150) the at least one determined impact reducing activity in the local energy supply system (34) and / or local production system (10).

2. The method according to claim 1, wherein the determining of at least one impact reducing activity comprises determining a number of activities for the local energy supply system and / or local production system that reduce the impact on the industrial process and further comprising analyzing the effect of the determined impact reducing activities and applying one or more of the determined impact reducing activities based on the analysis.

3. The method according to claim 1 or 2, wherein the local energy supply system (34) comprises renewable energy sources (40, 44), the energy generated by the local energy supply systems comprises renewable energy and the prediction of energy generation is made based on weather conditions in the time window.

4. The method according to claim 3, wherein the power supply system comprises a mixture of energy storage elements (48) and renewable energy sources (40, 44), where the renewable energy sources comprise wind (44) and / or solar (40) and / or geothermal energy sources.

5. The method according to claim 3 or 4, wherein the weather conditions comprise historical weather conditions corresponding to the time window.

6. The method according to claim 5 when depending on claim 4, wherein the at least one determined impact reducing activity comprises changing the mixture of energy storage elements (48) and renewable energy sources (40, 44).

7. The method according to claim 3 or 4, wherein the weather conditions comprise weather forecasts for the time window.

8. The method according to claim 7, wherein the at least one impact reducing activity comprises a change of operation in the production system (10) and / or operation in the energy supply system (34).

9. The method according to claim 8, wherein a change of operation in the production system (10) comprises an increase in production efforts when there is an energy surplus or a decrease in production efforts when there is an energy shortage.

10. The method according to claim 8 or 9 when depending on claim 4, wherein a change of operation of the energy supply system comprises storing at least some of the energy surplus in one or more energy storage elements when there is an energy surplus and compensating at least some of the energy shortage with energy from the one or more energy storage elements when there is an energy shortage.

11. An energy requirement analyzing arrangement (60) for analyzing production and energy requirements of an industrial process operated at at least one production site by a local production system (10) using energy generated by a local energy supply system (34), the energy requirement analyzing arrangement (60) comprising one or more processors (62) with computer program code causing said one or more processors to:- obtain energy consumption data and dynamics for the industrial process and the local energy generation at the production site;- generate a prediction of energy generation and a prediction of energy consumption in a time window;- estimate probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption;- generate an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site;- determine at least one activity for the local energy supply system and / or local production system that reduces the impact on the industrial process; and- apply the at least one determined impact reducing activity inthe local energy supply system and / or local production system.

12. The energy requirement analyzing arrangement according to claim 11, said at least one or more processors (62) implementing a digital environment module (68) representing the industrial process, a simulationmodule (70) and an impact mitigation module(72), wherein the digital environment module (68) is operative to receive said energy consumption data and dynamics and generate the prediction of energy supply and energy consumption, the simulation module (70) is operative to receive the predictions of energy supply and energy consumption, estimate the probabilities of energy surplus and energy shortage and generate the impact evaluation and the impact mitigation module (72) is operative to receive the impact evaluation and determine the at least one impact reducing activity.

13. A computer program for analyzing production and energy requirements of an industrial process operated at at least one production site by a local production system (10) using energy generated by a local energy supply system (34), the computer program comprising computer program code (66) causing, when being loaded into one or more processors (62) of an energy requirement analyzing arrangement (60), said one or more processors (62) to:- obtain energy consumption data and dynamics for the industrial process and the local energy generation at the production site;- generate a prediction of energy generation and a prediction of energy consumption in a time window;- estimate probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption;- generate an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site;- determine at least one activity for the local energy supply system and / or local production system that reduces the impact on the industrial process; and- apply the at least one determined impact reducing activity inthe local energy supply system and / or local production system.14- A computer program product for analyzing production and energy requirements of an industrial process operated at at least one production site by a local production system (io) using energy generated by a local energy supply system (32), the computer program comprising a computer-readable storage medium (74) comprising the computer program code (66) of claim 13.

15. An operations system comprising a local production system (10) operating an industrial process at at least one production site, a local energy supply system (34) generating energy for the local production system and an energy requirement analyzing arrangement (60), said energy requirement analyzing arrangement (60) being operative to:- obtain energy consumption data and dynamics for the industrial process and the local energy generation at the production site;- generate a prediction of energy generation and a prediction of energy consumption in a time window,- estimate probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption;- generate an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site,- determine at least one activity for the local energy supply system and / or local production system that reduces the impact on the industrial process; and- apply the at least one determined impact reducing activity in the local energy supply system and / or local production system.

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