Real Energy System Research Articles

Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events

Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%–73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems.

IT-Framework for Digital Energy Twin/Shadow Applications

Digital Energy Twins are IT systems, interconnecting sensor data, simulation models and user interfaces to formulate a virtual representation of the behavior of real energy systems. Digital Energy Twins are useful to predict the behavior of energy systems under varying boundary conditions and to optimize their operation considering economic and ecologic impact. Two different concepts of Digital Twins applicable to industrial energy systems were demonstrated: Digital Energy Twins and Digital Energy Shadows. While in literature, the term “Digital Twin” is widely used as synonym, for rather different applications involving simulations and virtual models connected to real-world data, this paper elaborates on the differences between digital twins and digital shadows in more detail. Given by the complexity of real-world energy systems (heat and electricity) and their implications on real time simulation, the concepts are demonstrated on different TRL levels. The results show the benefits and limitations of Digital Energy Twin and Digital Energy Shadow applications in relevant environments.

Allocation Analysis of the Energy Storage System in Integrated Energy Systems Considering the Carbon Emissions and Cost

With the increasing integration of renewable energy into the power grid, the variability in its power generation has a growing impact on the stable energy supply of the system. The utilization of energy storage systems (ESSs) has been considered as an efficient approach to mitigate this effect. However, due to the different characteristics of different ESSs, it becomes an urgent issue about how to allocate the ESSs or combine different types of ESSs to maximize its performance. This study has constructed a new method to compare the performance of different ESSs, and analyze the strengths and weakness of mainstream ESSs with the real energy systems in Wuhan. It demonstrated that for short-term storage options such as superconducting energy storage, lithium-ion battery storage, and supercapacitor storage, larger capacities (2000kWh) yield better results. Conversely, the optimal allocation of long-term options like lead-acid batteries, flow batteries, and compressed air energy storage systems, depends on specific characteristics and wind patterns. Furthermore, this research also reveals that when considering the combination of involving both long-term (lead-acid battery) and short-term (superconducting battery) storages, it cannot simultaneously achieve optimal performance in terms of operating cost or carbon emissions reduction. For hybrid combinations such as supercapacitor-lithium-ion battery (short-term-short-term) or compressed air-energy-flow battery (long-term-long-term), it is crucial to ensure that the type with superior performance possesses maximum capacity.

A Comparative Experimental Investigation of MPPT Controls for Variable Speed Wind Turbines

This work presents an experimental comparative investigation between Maximum power point tracking control methods used in variable speed wind turbines. In order to enhance the efficiency of the wind turbine system, the maximum power point tracking control has been applied for extracting and exploiting the maximum available wind power. Furthermore, two maximum power point tracking controls have been analyzed, developed, and investigated in real-time using Dspace. The first was optimal torque control without speed control, whereas the second was with speed control. The maximum power point tracking control performance comparison has been performed in a real-time experimental validation to illustrate the advantages of these control on the real wind energy system. The results have been achieved and discussed, where the power efficiency improvements appeared in the transit time and in the steady-state as well. In addition, the proposed optimal torque control for maximum power point tracking with speed control decreased the response time and oscillations, while it increased the power to an interval of 12,5% to 75% compared to that of strategy without speed control in the steady-state and transit state, respectively.

Recurrent machine learning based optimization of an enhanced fuel cell in an efficient energy system: Proposal, and techno-environmental analysis

Real hybrid energy systems based on developing fuel cell (FC) technology are promising in addressing energy and environmental problems. Before looking at the research investigations, the earlier review studies are briefly examined to spot any gaps in the literature. In the present study, a novel scheme of fuel cells is introduced with higher efficiency and compatibility. The exhaust gases are then introduced to the transcritical carbon dioxide cycle for the production of more power and to extract heat from the intercoolers. The system is analyzed from the multiple objective functions aspect, and the net present value method is occupied for determining the system's payback period. The important design conditions are then put to the test to seek their effect on the overall system. The genetic algorithm is applied for the sake of optimization, and the efficiency is maximized while minimization of environmental impact and the cost of the system. The results indicate that maximum ηII and minimum of LCOP and ζ at the ideal point which equals to 73.8%, 0.24 $/MWh and 0.07 kg/kWh, can be reached correspondingly. Also, by changing the selling price of electricity from 0.19 $/kWh to 0.22 $/kWh, the net present value and the payback period reach 1329000 $ and 6.24 years, respectively.

A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants

Recently, clean solar energy has aroused wide attention due to its excellent potential for electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) is the basis of the production and transmission of electricity. However, the current works neglect the regional correlation characteristics of PVPG and few studies propose an effective framework by incorporating prior knowledge for more physically reasonable results. In this work, a hybrid deep learning framework is proposed for simultaneously capturing the spatial correlations among different regions and temporal dependency patterns with various importance. The scientific theory and domain knowledge are incorporated into the deep learning model to make the predicted results possess physical reasonability. Subsequently, the theory-guided and attention-based CNN-LSTM (TG-A-CNN-LSTM) is constructed for PVPG prediction. In the training process, data mismatch and boundary constraint are incorporated into the loss function, and the positive constraint is utilized to restrict the output of the model. After receiving the parameters of the neural network, a TG-A-CNN-LSTM model, whose predicted results obey the physical law, is constructed. A real energy system in five regions is used to verify the accuracy of the proposed model. The predicted results indicate that TG-A-CNN-LSTM can achieve higher precision of PVPG prediction than other prediction models, with RMSE being 11.07, MAE being 4.98, and R2 being 0.94, respectively. Moreover, the performance of prediction models with sparse data is tested to illustrate the stability and robustness of TG-A-CNN-LSTM.

Operational optimization for off-grid renewable building energy system using deep reinforcement learning

With the application of renewable energy in single office buildings, an increasing number of power grids require building systems coupled with renewable energy to realize off-grid operation. However, the uncertainty of renewable energy sources and the safety of the corresponding energy storage equipment have become major challenges for these systems. Reinforcement learning has made considerable progress in the field of building control as an advanced control algorithm; however, research on its application to the off-grid operation of renewable energy systems, particularly the specific reward function design is limited.Therefore, this study considered the off-grid operation of a renewable building energy system and the safety (prevention of deterioration) of its battery as optimization goals. This study is based on a real building energy system, through the operation control of generators, solar photovoltaics, and batteries to achieve optimization purposes. Aiming at these two optimization goals, this study introduces a detailed reward function design and complete reinforcement learning workflow through a Gaussian distribution. Two deep reinforcement learning (DRL) algorithms were trained and verified by offline reinforcement learning based on the measured data-sets of actual existing buildings in Japan. The results show that the proposed reinforcement learning design can better achieve the two optimization goals of off-grid operation and battery safety under ordinary and extreme conditions. On off-grid operation tasks, the best DRL algorithm can achieve a mean hourly grid power purchase error of less than 2 kWh for the entire optimization window. Furthermore, the proposed deep reinforcement learning algorithms can simultaneously maintain a maximum average 7.72 h of battery unsafe state over the entire 168-hour optimization window.

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

With the rapid growth of wind power penetration into modern power grids, wind speed forecasting plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the existing wind speed forecasting methods are modeled as black boxes, which are very complicated and cannot be written down explicitly due to the complex fluctuation characteristics of wind speed series. To this end, this study proposes a novel direct method based on an explainable neural network (xNN) for deterministic and probabilistic wind speed forecasting. It can theoretically extract the nonlinear mapping features in wind speed, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Then, the uncertainties in wind speed are statistically synthesized via the kernel density estimation method. Finally, we use wind speed data from real wind farms in Belgium to verify the feasibility and effectiveness of the proposed method. The simulation results demonstrate that it is not only able to accurately extract the non-stationary feature in the wind speed series but also superior to other benchmark algorithms in prediction accuracy. Therefore, the proposed method has a high potential for practical applications in real electric power and energy systems.

100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power

This paper explores a new multi-stage robust scheduling method for cascade hydropower system considering nonanticipativity. In the existing scheduling methods, the nonanticipativity and robustness of solutions are not considered simultaneously, which may result in infeasibility in the actual operation of energy system. And, due to concerns about operation security, the available reserve of the hydropower system are not fully utilized. To this end, this paper proposes a novel multi-stage robust scheduling method for cascade hydropower system to guarantee the feasibility of scheduling solutions and increase the utilization of hydropower reserves. Specifically, first, an important proposition is presented to accurately describe the relationship between the hydropower output and the operation security of electricity system, indeed, the nonanticipativity and robustness are considered against the uncertainties of loads and renewable outputs. Second, based on this proposition, a pre-scheduling model of cascade hydropower is established to ensure the security of the whole energy system against the uncertainties of natural inflows. Last, within the safe ranges, a real-time economic dispatch model is solved to obtain final scheduling decisions that guarantee both operation security and economy of energy system. Numerical tests implemented on a real energy system verify the effectiveness of proposed method.

The EPLANopt model for Favignana island's energy transition

Energy costs, carbon dioxide emissions, security of supply and system stability are common challenges in small islands all over the world. The European Union identified islands as perfect sites to implement innovative solutions to boost the energy transition towards a sustainable, independent, secure and low carbon energy system. In this framework, energy planning is an indispensable tool to optimally design the future energy system selecting proper renewable energy sources as well as the optimal flexibility strategies such as electric energy storage or sector coupling solutions. Energy system modelling represents one of the most used method for energy planning; indeed, energy models enable to simulate the real energy system functioning as well as its operation costs. Nevertheless, not many researches using multi-objective optimisation have been applied to insular case studies. In this paper, the EPLANopt model is applied to the case study of the Favignana Island in order to investigate the optimal configurations of the island energy system in 2050 with a multi-objective analysis. In order to appropriately analyse the case study of a non-interconnected island, an additional constraint is analysed to preliminary consider the system stability. The model is used to evaluate different energy mix, based on high penetration of renewables, considering several solutions for handling the excess electricity production (namely, electricity energy storage, power to heat and power to transport solutions) and to improve the overall energy efficiency (i.e. solar collectors and heat pumps). Results show that sector coupling solutions would lead to much greater impact in terms of carbon avoidance and economic savings managing the non-dispatchable renewable generation and maintaining the critical excess electricity production within feasible values. Results show that Favignana should indeed bet on photovoltaic and if vehicle-to-grid strategies are largely adopted the need for electricity storages is strongly reduced.

Role of Hydrogen in a Low-Carbon Electric Power System: A Case Study

The European Union set a 2050 decarbonization target in the Paris Agreement to reduce carbon emissions by 90–95% relative to 1990 emission levels. The path toward achieving those deep decarbonization targets can take various shapes but will surely include a portfolio of economy-wide low-carbon energy technologies/options. The growth of the intermittent renewable power sources in the grid mix has helped reduce the carbon footprint of the electric power sector. Under the need for decarbonizing the electric power sector, we simulated a low-carbon power system. We investigated the role of hydrogen for future electric power systems under current cost projections. The model optimizes the power generation mix economically for a given carbon constraint. The generation mix consists of intermittent renewable power sources (solar and wind) and dispatchable gas turbine and combined cycle units fueled by natural gas with carbon capture and sequestration, as well as hydrogen. We created several scenarios with battery storage options, pumped hydro, hydrogen storage, and demand-side response (DSR). The results show that energy storage replaces power generation, and pumped hydro entirely replaces battery storage under given conditions. The availability of pumped hydro storage and demand-side response reduced the total cost as well as the combination of solar photovoltaic and pumped hydro storage. Demand-side response reduces relatively costly dispatchable power generation, reduces annual power generation, halves the shadow carbon price, and is a viable alternative to energy storage. The carbon constrain defines the generation mix and initializes the integration of hydrogen (H2). Although the model rates power to gas with hydrogen as not economically viable in this power system under the given conditions and assumptions, hydrogen is important for hard-to-abate sectors and enables sector coupling in a real energy system. This study discusses the potential for hydrogen beyond this model approach and shows the differences between cost optimization models and real-world feasibility.

Optimal control strategy of battery-integrated energy system considering load demand uncertainty

Uncertain load behavior affects the energy system operation scheduling in battery-integrated energy systems (BIES) due to the direct relation between optimal battery scheduling and load demand in the forthcoming time interval. In this paper, the optimal schedule of BIES is investigated with considering load demand uncertainty. The previous works have introduced a cost function and found optimal strategies using evolutionary optimization algorithms, which may fall in local minima. Against the previous works and in order to avoid falling in local minima, a closed-form expression is presented in this paper by analyzing a mathematically-modeled forecast error and considering battery characteristics. In the proposed method, the optimal battery charging/discharging state is analytically obtained based on forecast error and system parameters without the need to use numerical approximation algorithms. The applicability of the proposed scheduling strategy in the real energy system is shown by applying it to online optimal control of the battery in a real system with load demand forecast error, and also by comparing the results with previous methods. The proposed approach leads to the globally-optimal solution for battery scheduling and also shrinks the computational complexity of the BIES scheduling problem.

Cost-Effectiveness of Carbon Emission Abatement Strategies for a Local Multi-Energy System—A Case Study of Chalmers University of Technology Campus

This paper investigates the cost-effectiveness of operation strategies which can be used to abate CO 2 emissions in a local multi-energy system. A case study is carried out using data from a real energy system that integrates district heating, district cooling, and electricity networks at Chalmers University of Technology. Operation strategies are developed using a mixed integer linear programming multi-objective optimization model with a short foresight rolling horizon and a year of data. The cost-effectiveness of different strategies is evaluated across different carbon prices. The results provide insights into developing abatement strategies for local multi-energy systems that could be used by utilities, building owners, and authorities. The optimized abatement strategies include: increased usage of biomass boilers, substitution of district heating and absorption chillers with heat pumps, and higher utilization of storage units. The results show that, by utilizing all the strategies, a 20.8% emission reduction can be achieved with a 2.2% cost increase for the campus area. The emission abatement cost of all strategies is 36.6–100.2 (€/tCO 2 ), which is aligned with estimated carbon prices if the Paris agreement target is to be achieved. It is higher, however, than average European Emission Trading System prices and Sweden’s carbon tax in 2019.

Input data reduction for microgrid sizing and energy cost modeling: Representative days and demand charges

Computational time in optimization models scales with the number of time steps. To save time, solver time resolution can be reduced and input data can be down-sampled into representative periods such as one or a few representative days per month. However, such data reduction can come at the expense of solution accuracy. In this work, the impact of reduction of input data is systematically isolated considering an optimization which solves an energy system using representative days. A new data reduction method aggregates annual hourly demand data into representative days which preserve demand peaks in the original profiles. The proposed data reduction approach is tested on a real energy system and real annual hourly demand data where the system is optimized to minimize total annual costs. Compared to the full-resolution optimization of the energy system, the total annual energy cost error is found to be equal or less than 0.22% when peaks in customer demand are preserved. Errors are significantly larger for reduction methods that do not preserve peak demand. Solar photovoltaic data reduction effects are also analyzed. This paper demonstrates a need for data reduction methods which consider demand peaks explicitly.

Echo state network based ensemble approach for wind power forecasting

Accurate and reliable wind power forecasting is of great significance for the economic and safe operation of electric power and energy systems. However, due to the negative factors such as inaccurate numerical weather prediction and frequent ramping events, previous wind power prediction methods are difficult to meet actual needs for practical applications. To this end, this paper originally proposes a new wind power prediction method with high accuracy based on echo state network. This method is a hybrid of wavelet transform, echo state network and ensemble technique. Wavelet transform is used to decompose raw wind power time series data into different frequencies with better outliers and behaviors. Then, we adopt the echo state network to automatically learn the nonlinear mapping relationship between input and output in each frequency. Later, ensemble technique is applied to deal with the model misspecification and data noise problems, which are common in wind power prediction, thereby reducing the uncertainty of wind power forecasting and improving prediction accuracy. Finally, we use wind power data from real wind farms in Belgium and China to verify the feasibility and effectiveness of the proposed method. The simulation results show that the proposed method is superior to other benchmark algorithms in prediction accuracy, which indicates that the method has high potentials for practical application in real electric power and energy systems.

A Testing Framework for the Monitoring and Performance Analysis of Distributed Energy Systems

The increasing presence of intermittent distributed generation (DG) based on renewable energy sources (RESs) is heavily affecting the operation and management of modern distribution grids. Even though the use of distributed energy storage systems (DESSs) and the integration of advanced demand response (DR) programs are expected to mitigate this problem, security, safety, economic, and technical reasons are limiting the tests of these mechanisms in working systems. The test in simulation and emulation environments has proved to be an effective solution for the development of advanced control and protection schema, but their use for the simulation of the typically unpredictable behavior of end-users seems at least questionable. This paper proposes a testing framework for the analysis of experimental control strategies of distributed energy systems. The framework, based on a service-oriented architecture, can be easily interconnected to different systems, including renewable generators and storage systems. This framework can be used to analyze the performance of real energy systems, working in near-to-real conditions. The framework has been used for a pilot test on a subset of the facilities of the eLUX laboratory of the University of Brescia. The proposed framework allows analyzing the energy performance of this system by means of proper key performance indicators (KPIs). The efficiency analysis of a battery energy storage system (BESS) is presented, by proposing KPIs to evaluate the effect of different control strategies. Further KPIs are also used to evaluate the effectiveness of the BESS for improving the energy self-consumption of a photovoltaic system.

Simplified Resonant Parameter Design of the Asymmetrical CLLC-Type DC Transformer in the Renewable Energy System via Semi-Artificial Intelligent Optimal Scheme

Asymmetrical CLLC -type dc transformers (ACLLC-type DCTs) are becoming more and more popular in the renewable energy system, thanks to the bidirectional power transmission (PT), high power density, and low-cost sensorless open-loop control. Nevertheless, the resonant frequency is not constant due to variations of the operation power and temperature in practice, which may make DCT lose its required voltage conversion gain and deteriorate the PT ability. This poses a challenge on the design of circuit parameters, especially when the open-loop scheme is usually recommended for the ACLLC-type DCT in the renewable energy system. Therefore, a semi-artificial intelligence (semi-AI)-based simplified parameter design approach is put forward in this paper for ACLLC-type DCT. It replaces all unknown parameters with two intermediate parameters through certain manipulations, and then utilizes a very simple computer-assisted procedure to optimally design the parameters of the ACLLC-type DCT. In addition, a detailed design example with a special planar transformer is presented via the aid of ANSYS Maxwell to achieve the desired resonant parameters of the ACLLC-type DCT. Finally, the proposed semi-AI-based method is experimentally demonstrated in a real renewable energy system prototype.

Optimization of a regional energy system including CHP plants and local PV system and hydropower: Scenarios for the County of Västmanland in Sweden

Providing the energy needs of the cumulatively increasing population has become a challenge for the regional energy systems in the world. The most critical challenge is to supply enough energy in the forms of heat and power during the cold and warm periods of the year with the lowest production cost and minimum environmental impacts. A solution is to increase the green energy supply from renewable energy resources such as solar, wind power, and hydropower. In order to apply this solution in the real energy system, potentials for clean energy supply in an optimized manner should be evaluated. In this study, an optimization model is developed for a regional energy system in the central part of Sweden. The studied system consists of Combined Heat and Power (CHP) plants and heat water boilers together with renewable energy supply from rooftop Photo Voltaic (PV) - solar collectors and regional hydropower plants. The General Algebraic Modeling System (GAMS) is used to create the model based on the Mixed Integer Linear Programming (MILP) method. The goal is to evaluate the influence of local renewable energy systems on the production planning of CHP plants in a region. Two different scenarios are investigated based on the extremes in energy supply and demand concerning the increased use of Electrical Vehicles (EVs) and more application of Heat Pumps (HPs) in the system. The results show that installation of rooftop PV systems has the potential to reduce the electricity import to the region; however, it will at the same time reduce the operation time of the CHP plans during the summer period. With increased use of HPs for heating, the shut off time for CHP plants is further increased. Increase in electric passenger cars penetration in the system has no impacts on the production profiles of the plants. The regional electricity demand grows significantly by more utilization of EVs and increased application of heat pumps in the studied system. The high electricity demand will mainly be satisfied by importing electricity from outside the region together with low production from CHP plants and the power generated by the rooftop PV systems and regional hydropower. The developed optimization model with studied scenarios can be applied to other energy systems to increase the knowledge of production planning and feasibility of a fossil fuel free energy system.

Economic benefit evaluation method for the micro-grid renewable energy system operation

The development of micro-grid renewable energy system in China has achieved rapid growth in recent years, and the micro-grid renewable energy system has been drawing more and more attention by its flexible operation. Due to the randomness, fluctuation, uncertainty of the wind and photovoltaic renewable generation, abundant flexibility is required to meet the needs of safe, reliable and independent operation of the micro-grid energy system. We need to connect large energy systems to accept outside assistance when the micro-grid renewable energy system is short of adjustment capability. Independent operation and network operation will affect the economic benefit of the micro-grid energy system, so it is practically meaningful to study on the economic benefit evaluation of the micro-grid renewable energy system. This paper proposes a micro-grid energy system operation simulation model about wind and photovoltaic generation, the uncertainty of which is tackled based on the scenario generation and extraction techniques. Based on the proposed indices, the economic benefit could be evaluated by simulating the micro-grid energy system operation. The proposed method is validated by a real micro-grid energy system.

A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response

Multi-vector microgrids that utilise several forms of energy storage are becoming popular in smart grid topologies due to their ability to cope with problems induced in the power network from the usage of distributed generation. While these microgrids appear to be pivotal in future energy systems, they impose several problems in the design and operation of the network mainly due to their complexity and the different properties that each energy subsystem has. In this work, we propose a novel, generic and systematic way of modelling the assets in a microgrid including the energy management method that is used to control the operation of these assets under multiple stochastic loads. This gives a unique tool that allows the testing/derivation of multiple energy management methods including demand side response and the usage of forecasting tools to optimise the performance of the system. A thorough study of the proposed method, using data from a real hybrid energy system (built in Greece), proves that the performance and efficiency of the system can be significantly improved while at the same time the requirement for external power supply or the consumption of fossil fuels can be reduced. The main concept is based on a state space modelling approach that transforms the power network into a hybrid dynamical system and the implemented energy management method into the evolution operator. The model integrates structural, temporal and logical features of smart grid systems in order to identify and construct multiple different energy management strategies EMS which can then be compared with respect to their ability to best serve the considered demands. Other than coping with several energy carriers, the model inherently accounts for forecasting, handles multiple random loads with time dependant importance and supports the use of demand side response strategies. Conclusions drawn from numerical case studies are used to demonstrate the advantages of the proposed method. In this work we clearly show that by using 20 different energy management methods and analysing their performance through a multi-criteria assessment approach we obtain non-trivial energy management approaches to support the operation of a multi-vector smart-grid considering variable external demands.