Complex Energy Systems Research Articles

Advanced Control and Optimization for Complex Energy Systems

Advanced Control and Optimization for Complex Energy Systems

Optimal design methodology for sizing a fuel cell/battery hybrid power source

This paper presents an optimal design methodology enabling to exhibit the best parameters of a complex energy system combing several components and their related control parts. It is based on a particle swarm optimization technique for component sizing, combined with optimal control to consider energy management constraints. This approximate resolution is valuable since it allows to achieve a robust and effective optimal design using low computational resources: it enables to tackle large search spaces in engineering time constraints. The selected use case is a fuel cell/battery hybrid power source based on a power-split parallel architecture. Its performance index is defined as the fuel consumption. Regarding this objective, the drivetrain components size and the control parameters values are both strongly coupled and physically constrained. In this context, the methodology makes a tradeoff between component sizing and energy saving. Simulation results show the relevance and robustness of this approach regarding different driving cycles and operating conditions. It validates the replicability of this method to other optimization problems in the field of energy optimization. A comprehensive review of the simulation tests highlights the present limits of this optimization and provides new perspectives for future works.

Introducing reinforcement learning to the energy system design process

Design optimization of distributed energy systems has become an interest of a wider group of researchers due the capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and wind. White box models, using linear and mixed integer linear programing techniques, are often used in their design. However, the increased complexity of energy flow (especially due to cyber-physical interactions) and uncertainties challenge the application of white box models. This is where data driven methodologies become effective, as they demonstrate higher flexibility to adapt to different environments, which enables their use for energy planning at regional and national scale.This study introduces a data driven approach based on reinforcement learning to design distributed energy systems. Two different neural network architectures are used in this work, i.e. a fully connected neural network and a convolutional neural network (CNN). The novel approach introduced is benchmarked using a grey box model based on fuzzy logic. The grey box model showed a better performance when optimizing simplified energy systems, however it fails to handle complex energy flows within the energy system. Reinforcement learning based on fully connected architecture outperformed the grey box model by improving the objective function values by 60%. Reinforcement learning based on CNN improved the objective function values further (by up to 20% when compared to a fully connected architecture). The results reveal that data-driven models are capable to conduct design optimization of complex energy systems. This opens a new pathway in designing distributed energy systems.

Energy audit for complex energy system simulated using TRNSYS software

Energy audit for complex energy system simulated using TRNSYS software

Development and experimental evaluation of grey-box models of a microscale polygeneration system for application in optimal control

Optimisation based economic despatch of real-world complex energy systems demands reduced order and continuously differentiable component models that can represent their part-load behaviour and dynamic responses. A literature study of existing modelling methods and the necessary characteristics the models should meet for their successful application in model predictive control of a polygeneration system are presented. Deriving from that, a rational modelling procedure using engineering principles and assumptions to develop simplified component models is applied. The models are quantitatively and qualitatively evaluated against experimental data and their efficacy for application in a building automation and control architecture is established.

A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve building energy performance and efficiency. Regarding thermal load prediction, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time, but they may not provide accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models because the NARX concept can address nonlinear system behaviors effectively based on its recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using the field data of an academic campus building at Mississippi State University (MSU). Results show that the proposed NARX-ANN model can provide an accurate and robust prediction performance and effectively address nonlinear system behaviors in the prediction.

Heuristic optimization for grid-interactive net-zero energy building design through the glowworm swarm algorithm

As buildings account for a substantial amount of energy worldwide, many developers encourage self-sustained net-zero energy buildings (NZEBs) to minimize buildings’ energy consumption and negative impacts on the environment. In contrast to regular buildings, NZEBs have interactive and complex energy systems that require synthetical evaluation of their complicated interplay with the grid. Many studies implemented various optimization approaches to improve the NZEBs energy system designed with a global optimal sizing solution. However, the optimal design is often infeasible due to the availability of proper equipment sizing on the markets. In addition, due to the uncertainty caused by different climate conditions, government feed-in-tariff subsidies, and availability of renewable systems, there could exist multiple distinctive design solutions for the same building. Therefore, this study intends to develop a heuristic multiple objectives algorithm based on the glowworm swarm mechanism to refine the optimization of the grid-interactive NZEB design. To validate the proposed method, a case study of the Hong Kong Zero Carbon Building was examined and three design variables (area of the photo-voltage system, installation power of the wind turbine system, and capacity of the thermal energy storage system) were optimized. The results suggest that the optimized design can outperform the baseline design in terms of operating costs and grid energy consumption. In addition, multiple local optima design solutions that represent different design strategies can be identified using the proposed non-linear model.

Adaptation of the ascendency theory to industrial systems

Growth and development of ecosystems are subject to restrictions. Besides exergy, other properties, such as ascendency, have been described as goal functions that can inform about the health of ecosystems and synthesize information about the energy and matter flows in relation to an ideal theoretical state. Ascendency is an index that quantifies both the growth and the degree of organization in an ecosystem, in which several subsystems interact with mass and energy flows. Ascendency is quantified in terms of mass and energy exchanges among different subsystems constituting an ecosystem. Its value increases when the activity of the ecosystem increases (amount of mass and energy exchanges increase) and when the ecosystem evolves to a higher complexity of its structure (level of integration of the subsystems). Exergy provides information about the quality of energy and is therefore very appropriate for evaluating the thermodynamic efficiency of energy conversion processes. Growing concerns about energy efficiency have stimulated the development of techniques for the analysis, design, and diagnosis of complex energy systems based on the second law of thermodynamics. In this context, the set of methodologies called thermoeconomics was created, aimed at cost allocation (economic, thermodynamic, or environmental) and optimization of thermal systems based on thermodynamic concepts of system operation, which very often use exergy. Thermoeconomic analysis provides information on the interaction among the different components of energy systems and how energy resources are distributed throughout the internal mass and energy flows of the system. Herein the formulation of ascendency was adapted to industrial systems using exergy flows as the quantity of interest, employing the productive structure obtained from thermoeconomic analysis techniques to describe the production process of the system. Ascendency was then applied to a set of simple thermodynamic power systems based on the Rankine cycle to study the connection between energy (thermodynamic) efficiency and the “growth” and “development” of the power system. Four configurations with different interconnection levels between the equipment were studied, maintaining the final product of the power systems (net power) and the energy efficiency of the entire cycle constant, for comparison purposes. Moreover, different turbine efficiencies were also analyzed, maintaining a fixed system structure and constant net power produced, to obtain comparable results. It was verified that in the case of steam power plants based on the Rankine cycle, an increase in system complexity, maintaining the total plant production constant, increases ascendency as well as its potential for energy efficiency improvement. When different cases with the same interconnection level between equipment (same system structure), same net power production and different efficiencies are compared, the configuration with the highest ascendency value also presents a better potential for optimization.

Operation optimization and income distribution model of park integrated energy system with power-to-gas technology and energy storage

Power-to-gas (P2G) technology is considered as a new approach for clean energy consumption and energy conversion. However, because this technology must be combined with other energy systems to build a stable energy system, a reasonable income distribution method is necessary to guarantee this integration. Firstly, the operation optimization model of the park integrated energy system (PIES) and park independent energy system (PINES) with P2G are constructed for the first stage optimization, with the objective of maximizing net income. Secondly, the energy system performance evaluation indicators are developed to assess the system quantitatively in terms of the economic and environmental aspects. Thereafter, the income distribution models based on Shapley value and the improved Shapley value with operational risk factor are created, and the total income is optimally distributed at the second stage based on the first-stage optimization results. Finally, an industrial park in a province of China is selected for case analysis. The results show that (1) PIES can complement different systems, integrate the demands in heat, electricity, and gas, while realizing the electricity-gas-heat/electricity conversion and heat-electricity complementarity. (2) Income distribution using an improved Shapely value method is proposed, it overcomes the one-sidedness of transaction volume and lack of differences among participants, reflects the actual operational risk and the degree of contribution of participants to the whole system, and promotes the incentives of cooperation. (3) Based on the improved income distribution model, the income of the PS, P2G, and HS are redistributed. The income of P2G increased by ¥7,450, which reflects the important collaborative value of P2G. The willingness of system cooperation increased by 742.392%. Therefore, the proposed operation optimization and income distribution model can enhance the incentives of participants want to cooperate under the premise of ensuring the maximum net income of the PIES. Moreover, it can be used as reference for the formulation of an optimal operation plan and income distribution of the complex energy system and it can provide a way to promote clean energy production. • The role of P2G as a new energy conversion way is studied. • The two-stage optimization model is constructed. • The first stage is operational optimization. • The second stage is income distribution. • The improved Shapley value method is applied in park integrated energy system.

Optimization and design of heat recovery system for aviation

Heat recovery remains an active topic in energy system research because of the potential benefits in energy consumption and energy efficiency. Among the recent advances, the intercooled recuperated heat recovery concept has provided significant advancements in energy conversion for gas turbine engines. However, when applying this concept to aero engines, the lack of available space in the engine and the low tolerance for pressure loss are critical challenges in this complicated energy system. Therefore, an improved structure for a complex heat recovery energy system called the intercooled recuperated turbofan engine is proposed. In addition, a novel systematic optimization design method based on multidisciplinary theories is introduced. The new heat recovery system design is designed and analyzed with respect to the installation space and the engine performance. Because the system is very complex and strongly coupled, a sensitivity analysis has been conducted to decouple the parameters of this non-linear problem. By optimization, the structure of the engine is modified to adapt the heat recovery system′s demand for space (through a change in air flow). Its energy saving benefits are compared to a traditional turbofan engine; the new, optimized design has a lower fuel consumption. It achieves a high overall efficiency with a lower overall pressure ratio. This systematic design method is extremely important for complicated energy systems: it addresses the necessary compromises between structure and performance, which remains a critical challenge for energy systems.

Cobalt-boron-oxide supported on N, P dual-doped carbon nanosheets as the trifunctional electrocatalyst and its application in rechargeable Zn-air battery and overall water-electrolysis

The development of trifunctional catalysts integrated with the catalytic activities of oxygen reduction reaction (ORR), oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) remains an important scientific challenge. The obvious preparation strategy is to load metal-based compounds on the non-metal element doped carbon catalyst. A compact interface between heterogeneous materials is the key to obtain efficient performance. Herein, the cobalt-boron-oxide nanosheets were embedded into the N, P dual-doped carbon nanosheets, and a compact composite catalyst (Co–B–O/NPC) was constructed using boron as a medium. The Co–B–O/NPC-50% catalyst exhibits the ORR catalytic activity surpassing Pt/C catalyst, the OER catalytic activity exceeding RuO2, and the excellent catalytic activity of HER. These three catalytic performances originated from synergy also exhibit good stabilities. The rechargeable zinc-air battery using Co–B–O/NPC-50% as the electrocatalyst exhibits the power density of 110.7 mW/cm2 and a stable charge-discharge cycling of more than 60 h. The overall water-splitting electrolyzer using Co–B–O/NPC as sole catalyst also exhibits excellent performance and stability. A complex energy-system that combines Zn-air battery, illumination and water-splitting was built due to trifunctional characteristics. This work not only presents a novel preparation strategy, but also can deepen the understanding of the structure-activity relationship of trifunctional electro-catalysts.

Are complex energy system models more accurate? An intra-model comparison of power system optimization models

Today, optimization models are by far the most popular choice when analyzing energy systems. Impressive advances in computer and data sciences have allowed for a multitude of complex energy system optimization models. In our manuscript, we assess the hypothesis of a positive correlation between complexity of a model and accuracy of its results. For this, we propose a framework based on alternative model formulations and apply it in a case study with 160 different, more or less complex, implementations of power system optimization models for dispatch and investment. Our results indicate that a certain degree of complexity is indeed necessary to obtain sufficiently accurate results. However, a careful balancing is also necessary for acceptable use of computational resources. In terms of partial load efficiency of conversion processes, our findings indicate that (two) different formulations should be used for dispatch and for investment decisions. Furthermore, a model including simple grid constraints comes with only minor increases in complexity compared to a model assuming a copper plate. Moreover, the combination of multiple technical constraints for conversion processes leads to a significant gain in accuracy while the combination of multiple time-coupling constraints is a major complexity driver.

Are open access models able to assess today's energy scenarios?

The current transition towards a low-carbon energy system requires an increasingly complex energy system framework. This is accompanied by the demand for high result reproducibility in order to provide transparency to decision-makers in terms of assumptions and methodological issues. Given this background, Open Access Models (OAMs) are increasingly entering the market that already have a high degree of diversity. This study analyses and compares the methodological framework of different OAMs to assess long-term energy scenarios. In a first step, selected OAMs are typified and characterised based on predefined criteria. In general, the analysis reveals that OAMs with a high level of accessibility appear to have a rather low level of complexity and often focus on the analysis of a single target year. In a second step, we underline our findings of the model overview by applying a well-established OAM (DESSTinEE - Demand for Energy Services, Supply and Transmission in EuropE) to a current energy scenario for Germany. Overall, we conclude that current OAMs can already be applied to a large variety of research questions. However, comparing OAMs to conventional models applied in the field of energy system analysis reveals that there is still a significant performance gap in terms of the degree of methodological sophistication.

Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

Buildings play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large-scale energy-management strategies from the supply side to the consumer side. When buildings integrate local renewable-energy generation in the form of renewable-energy resources, they become prosumers, and this adds more complexity to the operation of interconnected complex energy systems. A class of methods of modelling the energy-consumption patterns of the building have recently emerged as black-box input–output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produced by nondeterministic processes underlying energy consumption. We present an application of a class of neural networks, namely, deep-learning techniques for time-series sequence modelling, with the goal of accurate and reliable building energy-load forecasting. Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects, and are considered suitable for further use in future in situ energy management at the building and neighborhood levels.

Heat tubes: Conduction and convection

This note offers a clear view of heat transfer fundamentals in terms of ‘heat tubes’, which are macroscopic architectures for the flow of energy through complex energy systems. The note focuses on heat tubes with conduction and convection, although heat tubes with radiation through stacks of radiation shields have also been treated in the literature. It is shown how to shape a heat tube and how to cool it along its length so that its thermodynamic performance evolves. Several instances of confusion are clarified along the way.

Application of the second law of thermodynamics to control: A review

Abstract The development of recently-designed energy efficient components and complex energy concepts has heightened the need to develop advanced control strategies. Exergy-based control strategies are among the recently introduced control strategies, which apply the second law of thermodynamics to control. Exergy is defined as the maximum available work that can be extracted from a system during a process that brings the system into equilibrium with its environment. An exergy analysis can be applied as a powerful thermodynamic technique for assessing and optimizing the efficiency of complex energy systems. A lot of research has been carried out on the exergy analysis of energy systems, but they are typically based on steady-state assumptions. These studies may be helpful to identify or rank the sources of irreversibilities in an energy system, but usually, the main challenge that is faced in real-life is how to efficiently control the energy systems, which are naturally dynamic. Most of the time, the inefficient components of a system are either known or can readily be found, but due to financial concerns, replacing those components with efficient ones or even changing the current system layout in order to maximize the efficiency is not feasible, while achieving the most efficient operation of an imperfect system is always a desire. Despite being common, the steady-state analyses are not capable of offering practical solutions to the real life problems. Although dynamic exergy analysis has recently attracted the attention of researchers, not many studies have been performed on this topic, among them only a few are really control-oriented. Therefore, there is still a real need to evaluate the practical application of exergy from a control point of view, especially on a building scale. This paper aims at presenting a comprehensive literature review of the studies performed on the application of exergy to control, highlighting the still existing gaps. The paper presents an introduction to exergy. The challenging topic of the reference environment is then discussed followed by a thorough review of the application of exergy to control. A summary of control techniques used in the literature, as well as the applications and research focuses of the literature is also presented.

A Practical Tool to Generate Complex Energy System Configurations Based on the SYNTHSEP Methodology

Traditional fossil fuelled power plants are commonly based on steam Rankine cycle or Brayton Joule cycle. Using water or air as working fluid is obviously the most obvious choice for the wide availability of these substances in nature. However, the scarcity of natural energy sources and the strong need of reducing environmental impact have necessarily drawn the research to new energy systems configurations operating with other working fluids, which are able to recover lower temperature sources, such as Sun or industrial wasted heat. The variety of new working fluids (refrigerants or organic fluids) widens the choice to a variety of configurations that can be tailored to the specific source characteristics and boundary constraints. It is not always easy or even possible to conceive the best configuration for given specifications with the mere experience of a common designer. To design a new system configuration, the designer normally uses some “non-codified rules” deriving from his knowledge of basic thermodynamics and energy engineering. This paper aims instead at showing a practical tool that is based on a new methodology, named SYNTHSEP, to generate new energy system configurations. This methodology starts from the simple thermodynamic cycles operated by a given fluid made up of the four fundamental processes (compression, heating, expansion and cooling) and uses a rigorous set of codified rules to build the final system configuration. The paper presents the basics of the new methodology and how it has been implemented in a practical tool that simply requires the information about the elementary cycles and their shared processes as input data.

How to conceptualise energy law and policy for an interdisciplinary audience: The case of post-Brexit UK

Interdisciplinary energy research is essential. It advances our understanding of potential transitions from high to low carbon energy systems. However, it is easier to propose than deliver. It requires translation into a simpler language, to aid communication, but not at the expense of the conceptual language that drives our understanding of complex energy systems. We combine legal, political science, and policy studies to show how to balance the need to communicate accessibly and recognise legal and policymaking complexity. We begin with a statement so accepted in legal studies that it has become a truism: the law in the books is not the same as the law in action. The allocation of legal competences is only one influence on policymaking in a complex system. We describe three key ways to conceptualise this relationship between law, policy, and energy systems, focusing on the: (1) ‘on paper’ legal separation of powers between different governments, (2) interaction between law and policy in practice, including blurry boundaries between formal responsibility and informal influence, and (3) role of law as one of many contributors to policymaking. We use these approaches to explain the implications of Brexit for UK energy policy.

Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison

Modeling time-varying operations in complex energy systems optimization problems is often computationally intractable, and time-series input data are thus often aggregated to representative periods. In this work, we introduce a framework for using clustering methods for this purpose, and we compare both conventionally-used methods (k-means, k-medoids, and hierarchical clustering), and shape-based clustering methods (dynamic time warping barycenter averaging and k-shape). We compare these methods in the domain of the objective function of two example operational optimization problems: battery charge/discharge optimization and gas turbine scheduling, which exhibit characteristics of complex optimization problems. We show that centroid-based clustering methods represent the operational part of the optimization problem more predictably than medoid-based approaches but are biased in objective function estimate. On certain problems that exploit intra-daily variability, such as battery scheduling, we show that k-shape improves performance significantly over conventionally-used clustering methods. Comparing all locally-converged solutions of the clustering methods, we show that a better representation in terms of clustering measure is not necessarily better in terms of objective function value of the optimization problem.

Thermal-hydraulics and neutronic code coupling for RELAP/SCDAPSIM/MOD4.0

The design and analysis of energy systems requires robust and reliable computer codes that produce realistic results. The governing equations and closure models must represent the physical behaviors of the energy systems. Nuclear power plants, as the quintessential example of complex energy systems, must be modeled with such high-fidelity and accurate nuclear system codes in which thermal-hydraulics, neutronics and fuel performance models must be coupled for in order to produce high-fidelity predictions. Most commonly, the interplay between the various subsystems of a nuclear power plant (nuclear fuel, fluids, neutronics, control rods, power conversion, etc.) is modeled through a combination of different codes that are externally coupled.The objective of this paper is to present work that couples the RELAP/SCDAP SIM/MOD4.0 code with the NESTLE neutronic code within the 3DKIN package, thus providing the latter with updated neutron kinetic capabilites. The version of the RELAP/SCDAPSIM/MOD4.0 code used in this work has been developed by Innovate System Software (ISS) and is the latest in the series of such code versions developed as part of the international SCDAP Development and Training Program (SDTP) for best-estimate analysis to model reactor transients including severe accident phenomena. The new feature of 3DKIN enhances the simulation of the Nuclear Power Plants (NPP) response under accidental and operational scenarios in which high reliability of neutronic feedback is needed.The RELAP/SCDAPSIM code is described and the improvements implemented in it are presented, including nodal kinetics library as well the coupling method that is applied. Finally, a BWR transient with unexpected injection of subcooled water as specified in an OECD Benchmark has been simulated in order to assess the reliability of the package and the performance of the coupling. Results are compared to those of the other OECD benchmark participants. The comparison of fuel temperature, effective multiplication factor (keff) and power distribution results display good agreement with values within the range of those of other participants.