Energy Management Performance Research Articles

Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks

Power distribution grids experiences proliferation of solar photovoltaics (PV) at the system edge. However, its counterpart of sparse meter deployment provides insufficient monitoring of PVs, for which the potential violations challenge the operators for energy management and stable operation. Some previous works use satellite imagery to detect distributed PVs for the easy access of data. However, their PV localization methods rely on label-rich area with unitary background/environment to implement well; even further/harder, they do not provide precise metered-PV detection and quantification to estimate/know PV generation outputs in unobservable area, which is essential to prevent the edge from excessive two-way power flow and other violations. Thus, we combine the two steps of detecting PV existence and quantify PV amount into one classification task. To boost the classification performance in unobservable edge area, we construct a generative adversarial network that simultaneous augments the diversity of labelled PV satellite images and embed distinct PV characteristics/features for training the classifier. Furthermore, the PV localization and quantification result is combined with geographic information, historical weather conditions and neighboring generation patterns to estimate power output at the system edge. We validate the proposed approaches on PV systems in the southwest of the U.S. Experiment results show high accuracy and robustness in predicting distributed solar power without sufficient prior information.

Two-Stage Data-Driven Optimal Energy Management and Dynamic Real-Time Operation in Networked Microgrid Based Deep Reinforcement Learning Approach

Two-Stage Data-Driven Optimal Energy Management and Dynamic Real-Time Operation in Networked Microgrid Based Deep Reinforcement Learning Approach

Optimal planning and configuration of adiabatic-compressed air energy storage for urban buildings application: Techno-economic and environmental assessment

As urbanization and demand for energy increase, the importance of localized renewable energy resources and energy storage system solutions becomes more prominent. Adiabatic-compressed air energy storage (A-CAES) has been identified as a promising option, but its effectiveness in decentralized applications is not widely concerned. This study aims to plan and design a decentralized A-CAES system to enhance its significance within urban building infrastructure. The focus is on the optimal design of a decentralized A-CAES system for urban building utilization through a comprehensive analysis of its thermodynamic, techno-economic, and environmental aspects. A sizing-designing approach inducing simulation and optimization is proposed to customize the A-CAES system according to the specific application requirement. To do so, this study investigates several energy management operation strategies (EMOS) toward different potential applications of decentralized A-CAES to maximize the system's value and adaptability during the project's lifetime. Multiple scenarios concerning managing solar PV-surplus power are proposed to investigate the overall performance of the proposed model. Additionally, a sensitivity analysis (post-optimization) is conducted to ensure the model's applicability across different case studies. The results demonstrate that an energy cost saving in the range of 0.015–0.021 $/kWh is achieved for the optimal hybrid system in which the A-CAES system is planned for solar photovoltaic (PV) integration and seasonal load shifting, leading to shaving the grid peak demand. Adopting such an A-CAES-PV hybrid system allows for achieving a 52 % electrical load management ratio and 65 % carbon emission reduction compared to the primary power system (grid). Furthermore, under the worst-case scenario (zero selling back), such an optimal hybrid energy system (HES) achieves a PV self-consumption rate of around 92 % and a payback time of 15.5 years. This analysis provides useful insights for policymakers, building owners, and energy planners interested in implementing sustainable and energy-efficient solutions, especially in the feasibility study phase.

Cost-effective energy management of an islanded microgrid

The interest in the integration of distributed energy resources in microgrids increased significantly in the last decade. The stochastic nature of some energy sources and the dynamic power demand has brought difficulties regarding the optimal control of microgrids. This current study addresses the energy management challenge in an islanded hybrid energy microgrid that includes three types of renewable energy resources (photovoltaic, geothermal and biomass) and a battery storage system, using intelligent management methods that optimize energy production costs. The proposed intelligent management methods are based on cost function minimization. These ensure optimal energy management operation under uncertain weather conditions, keeping to the minimum the price of the energy produced by the microgrid. Due to the erratic nature of solar irradiance and considering that solar energy is cheaper than its counterparts (geothermal and biomass in this case), forecasting the photovoltaic power production is an essential task for guaranteeing optimal microgrid operation. Day-ahead photovoltaic power forecasts are issued based on solar irradiance forecasts provided by the Global Forecasting System and further integrated into the day-ahead scheduling of the microgrid. This algorithm, together with the cost-optimization algorithms, gives intelligence to the management protocol generating the input parameters (power and operating time for each generating unit) for the microgrid Energy Management Controller. Different cost optimization methods are studied and compared, including mathematical models, such as Mixed Integer Linear Programming, or nature-inspired optimization algorithms, such as Genetic Algorithm, Harmony Search Algorithm, and Particle Swarm Optimization. It is shown that these algorithms provided suitable solutions for the energy balance and scheduling ensuring minimal operation costs for the microgrid. The study also highlights the differences and limitations of these algorithms and shows a method on how these energy management methods can be integrated into a real-world microgrid providing a step forward in the development of intelligent microgrids.

Microgrid operational energy management with plug-in hybrid electric vehicles charging demand

Microgrid operational energy management with plug-in hybrid electric vehicles charging demand

Consumption–Production Profile Categorization in Energy Communities

Energy Transition is changing the renewable energy participation in new distributed generation systems like the Local Energy Markets. Due to its inherent intermittent and variable nature, forecasting production and consumption load profiles will be more challenging and demand more complex predictive models. This paper analyzes the production, consumption load profile, and storage headroom% of the Cornwall Local Energy Market, using advanced statistical time series methods to optimize the opportunity market the storage units provide. These models also help the Energy Community storage reserves to meet contract conditions with the Distribution Network Operator. With this more accurate and detailed knowledge, all sites from this Local Energy Market will benefit more from their installation by optimizing their energy consumption, production, and storage. This better accuracy will make the Local Energy Market more fluid and safer, creating a flexible system that will guarantee the technical quality of the product for the whole community. The training of several SARIMAX, Exponential Smoothing, and Temporal Causal models improved the fitness of consumption, production, and headroom% time series. These models properly decomposed the time series in trend, seasonality, and stochastic dynamic components that help us to understand how the Local Energy Market consumes, produces, and stores energy. The model design used all power flows and battery energy storage system state-of-charge site characteristics at daily and hourly granularity levels. All model building follows an analytical methodology detailed step by step. A benchmark between these sequence models and the incumbent forecasting models utilized by the Energy Community shows a better performance measured with model error reduction. The best models present mean squared error reduction between 88.89% and 99.93%, while the mean absolute error reduction goes from 65.73% to 97.08%. These predictive models built at different prediction scales will help the Energy Communities better contribute to the Network Management and optimize their energy and power management performance. In conclusion, the expected outcome of these implementations is a cost-optimal management of the Local Energy Market and its contribution to the needed new Flexibility Electricity System Scheme, extending the adoption of renewable energies.

A Systematic Literature Review of State of Health and State of Charge Estimation Methods for Batteries Used in Electric Vehicle Applications

In recent years, artificial intelligence and machine learning have captured the attention of researchers and industrialists in order to estimate and predict the state of batteries. The quality of data must be good, and the source of data must be the same for different models’ performance comparisons. The lithium-ion battery is popularly used because of its high energy density and its compact size. Due to the non-linear and complex characteristics of lithium-ion batteries, electric vehicle users have to know about battery health conditions. Different types of state estimation methods are used, namely, electrochemical-based, equivalent circuit model (ECM) based, and data-driven approaches. This paper is a survey of electric vehicle history, different battery chemistries, battery management system (BMS) basics and key challenges and solutions in BMS, and in-depth discussions about other battery state of charge and state of health estimation methods. Research trend analysis, critical analysis of this work, limitations, and future directions of existing works are discussed. This paper also provides information on the open-access available datasets of different battery chemistry for a data-driven approach. This paper highlights the key challenges of state estimation techniques. Knowledge of accurate battery state of charge (SOC) provides critical information about remaining available energy. In comparison, battery state of health (SOH) indicates its current health condition, remaining lifetime, performance, and proper energy management of the electric vehicles.

Effects of Price Range Variation on Optimal Sizing and Energy Management Performance of a Hybrid Fuel Cell Vehicle

The usage of multi-objective cost functions (MOCFs) in sizing and energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs) has expanded due to the participation of multiple technological and economic disciplines. To better understand the impact of price fluctuation on the component size and EMS of an FCHEV, this article proposed a sensitivity analysis methodology. First, a two-step optimization approach that considers hydrogen consumption, system degradation, and trip cost is used to minimize a MOCF of the Can-Am Spyder electric motorcycle simulator. Then, an effect analysis is carried out for the cost-optimal results under two driving profiles to understand the link between cost variation and system performance. These simulations indicate that each might result in different system sizes and EMS compromise. After that, an online optimization EMS based on sequential quadratic programming is used on a reduced-scale hardware-in-the-loop configuration to evaluate the simulation results with varied weights. Experimental results indicate that when an adequate size is used for each pair of weights, the EMS results in a 6% decrease in the trip cost.

Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture

Accurate building energy consumption forecasting is crucial for developing efficient building energy management systems, improving energy efficiency, and local building energy supervision and management. However, short-term building energy consumption forecasting is challenging due to highly non-smooth and volatile trends. In this paper, we present a novel methodology that combines interpretable decomposition methods with an interpretable forecasting model. We first illustrate a daily energy consumption pattern recognition (DECPR) method, which decomposes daily energy consumption patterns into interpretable energy consumption subsequences. To achieve satisfactory forecasting performance, we design the vector representation of each subsequence as a static input to the temporal fusion transformers (TFT) model. This vector representation integrates the DECPR method into the TFT model. The TFT model produces interpretable outputs, such as the attention analysis of different step lengths and the visualization of the importance ranking of exogenous variables, including meteorological data, calendar information, and the vector representation. Empirical studies demonstrate that our proposed DECPR-TFT system outperforms comparable models with a mean absolute percentage error (MAPE) of 6.11%, which is significantly lower than other models. These interpretable outputs provide valuable insights for researchers seeking to develop energy-saving operation strategies in buildings. Overall, our methodology offers a promising solution for short-term building energy consumption forecasting that can contribute to more efficient building energy management and energy-saving operation strategies.

Centralized MPC for Home Energy Management With EV as Mobile Energy Storage Unit

Electric vehicle (EV) is commonly considered as an electric load in a residential energy network. However, the large capacity EV battery can be used as electric storage when the EV is plugged in at home. While model predictive control (MPC) offers an efficient and reliable control mechanism for a home energy management system (HEMS), the uncertainty related to the availability of an EV at home poses an interesting challenge for the MPC optimization strategy. In this article, the thermal comfort and energy management performance of a centralized MPC-based HEMS is presented for such a scenario where an EV is used as a mobile energy storage unit in a home energy network. The MPC-based HEMS simultaneously controls the zone-based heating system consisting of a heat pump and baseboard units along with the energy flow among the different components of the home energy network. This network comprises the home baseload, charging and discharging of the home-battery and the EV battery, in-house solar energy generation and storage. The MPC-based HEMS optimizes the buying/selling of energy from/to the grid based on time of use electricity rates. A Monte-Carlo based uncertainty analysis is presented here to estimate the robustness of the MPC against the randomness in the EV arrival and departure schedule. Finally, the responsiveness of the centralized MPC is showcased using a vehicle to home communication scenario where the owner notifies the home network regarding their arrival within a short interval, along with the preferred indoor temperature upon arrival.

How can industrial heat decarbonisation be accelerated through energy efficiency?

The ongoing energy transition necessitates commitments from various sectors to utilise resources more efficiently. Amongst these, the industrial sector, which is associated with high energy and resource consumption and emissions, has been attracting attention specifically aimed at performance enhancements and continuous progress in energy utilisation. The continued evolution of industrial operations and performance requires energy efficiency measures to be developed and implemented. Diverse portfolios of products, wide-ranging types of equipment, processes and, subsequently, plants, are adopted in the industrial sector, such that energy efficiency measures vary widely, along with their effectiveness, technological maturity, technical and economic impact. It remains a challenge to select the optimal energy efficiency measure(s) for a specific industry, plant and process, given the specific asset requirements. In this context, the development of systematic approaches for identifying optimal energy efficiency measures is of great interest. In this vision paper, we present an assembly of available systematic tools for advancing the energy efficiency of plants and sites in the industrial sector. The contribution of this work to the field of industrial heat decarbonisation arises from developing and proposing the use of a new holistic framework as a guide for the continuous performance improvement of thermal-energy-intensive industries through a series of energy efficiency measures and actions. Specifically, the framework suggests initiating efforts from a proposed top-down peer benchmarking practice aimed at identifying gaps in energy-efficiency performance across products, plants, processes and equipment. In a second stage, recommendations are made in form of a list of steps to close these gaps, starting with conducting equipment gap closure analyses, followed by design improvement studies at the process, plant and site levels using tools such as pinch analysis, steam system optimisation and residual waste heat recovery. We finally propose that simultaneous attention should be given to operational energy management programs along with a sequence of recommended actions to minimise deviations from the targeted energy-efficiency performance. In this vision paper, key technical tools needed to achieve the goal of continuous heat decarbonisation through energy efficiency are reviewed, and the organisational and management aspects required for effective energy targeting and management, and stakeholder engagement are addressed, based upon which relevant research challenges and opportunities are identified.

Maiden Application of the Slime Mold Algorithm for Optimal Operation of Energy Management on a Microgrid Considering Demand Response Program

Maiden Application of the Slime Mold Algorithm for Optimal Operation of Energy Management on a Microgrid Considering Demand Response Program

A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation

The energy consumption of major equipment in residential and industrial facilities can be minimized through a variety of cost-effective energy-saving measures. Most saving strategies are economically viable where several algorithms can be employed to reduce energy consumption to reduce costs to a considerable extent. Machine learning (ML) is one of these techniques. A review of recent research efforts concerning the application of ML strategies to energy conservation and management problems is presented in this study. In addition, ML approaches and strategies for energy-saving problems, management, technologies, and control methods have been discussed. A comprehensive review of all available publications is also used to make observations about past considerations. As a result, it has been concluded that ML is capable of solving a wide range of decision and management problems within a short period of time with minimal energy consumption. In addition, ML perspectives have been viewed from the perspective of emerging communication technologies, instruments, and cyber-physical systems (CPSs), along with the advancement of ultra-durable and energy-efficient Internet-of-Things (IoT) based communication sensors technology. Moreover, a comprehensive review of recent developments in ML algorithms is also included, including safe reinforcement learning (RL), Deep RL, path integral control for RL, and others not previously. Lastly, critical ML considerations such as emergency and remedial measures, integrity protection, fusion with existing robust controls, and combining preventive and emergent measures have been discussed. The implementation of recently applied ML, RL, and IoT strategies for energy management, conservation, and resilient operation is clarified in this paper. The proposed review highlights the advantages and drawbacks of the recent energy conservation strategies. Finally, the perspective solutions have been clarified to cope with the world direction for zero energy buildings.

Heat rectification, heat fluxes, and spectral matching.

Heat rectifiers would facilitate energy management operations such as cooling or energy harvesting, but devices of practical interest are still missing. Understanding heat rectification at a fundamental level is key to helping us find or design such devices. The match or mismatch of the phonon band spectrum of device segments for forward or reverse temperature bias of the thermal baths at device boundaries was proposed as the mechanism behind rectification. However, no explicit theoretical relation derived from first principles had been found so far between heat fluxes and spectral matching. We study heat rectification in a minimalistic chain of two coupled ions. The fluxes and rectification can be calculated analytically. We propose a definition of the matching that sets an upper bound for the heat flux. In a regime where the device rectifies optimally, matching and flux ratios for forward and reverse configurations are found to be proportional. The results can be extended to a system of N particles in arbitrary traps with nearest-neighbor linear interactions.

Explainable multi-agent deep reinforcement learning for real-time demand response towards sustainable manufacturing

The demand response (DR) plays a significant role in manufacturing system energy management and sustainable industrial development. Current literature on DR management for manufacturing systems has mostly focused on day-ahead production scheduling, whose effectiveness is limited due to the lack of flexibility to control the production line in real time. The development of reinforcement learning (RL) possesses huge potential for real-time production control to address the flexibility issue. However, since production is the top priority for any manufacturing system, a trustful and explainable RL for manufacturing system energy management that can ensure the production requirements is necessary for this application. This study proposes an explainable multi-agent deep RL method, where the analytical manufacturing system model is applied to decompose the system-level energy management objective and production requirement to the agent level. Based on the decomposed task, the agent can then form a safe action subset that is interpretable to achieve the original system-level production requirement while learning to reduce energy costs under DR. The proposed RL method, which is referred to as decomposed multi-agent deep Q-network (DMADQN), is applied to control a section of an automotive assembly line using one year of DR electricity price data to validate its performance. Results show that the proposed method ensures the achievement of the production requirement while providing better DR energy management performance in both RL training and testing phases. In addition, the proposed approach can outperform the day-ahead scheduling approach and save up to an additional 30.7% of energy costs under dynamic DR.

Multi-objective genetic algorithm based energy management system considering optimal utilization of grid and degradation of battery storage in microgrid

This paper develops intelligent energy management in Microgrid using forecasting-based multi-objective optimization using genetic algorithm framework. In this work, the energy storage system is included in Microgrid network, which is essential for effective energy management and smooth power transfer. The developed model incorporates the forecasted values of solar PV and wind generation obtained using time series long short-term memory network for the next 24 h and provides the optimal values of battery and grid powers to meet the deficit of power to meet the demand in the Microgrid. The proposed model incorporates grid power consumption cost and battery degradation cost as the objectives with battery status and renewables utilization as the constraints of the optimization model. The fuzzy decision-making strategy obtains the compromise solution in the Pareto optimal front from multiple solutions. The proposed model is investigated with fixed and variable grid tariff conditions achieving efficient performance in energy management with optimal utilization of renewables, grid and battery in the system for 24 hours-time horizons. The system parameters in the Microgrid network are also investigated in the time horizon with the optimal values of proposed model. The proposed intelligent energy management system model is tested in 2.5 MW PV/wind/energy storage Microgrid system in MATLAB 2020 simulation platform and experimental setup of 1 kW grid connected Microgrid with solar PV and battery.

Using Catalyst Mass-Based Clustering Analysis to Identify Adverse Events during Approach

Discovering and mitigating potential risks in advance is essential for preventing aviation accidents on routine flights. Although anomaly detection-based explanation techniques have successfully uncovered potential risks for proactive flight safety management, explaining group-scale precursors using these methods is challenging due to the assumption that risky flights are significantly fewer in number than normal flights, as well as the reliance on non-domain knowledge for hyperparameter adjustment. To characterize the group-scale precursors more accurately, we propose a novel technique called Catalyst Mass-Based Clustering Analysis (CMCA), which employs a composite entropy-energy dissipation index during approach to evaluate the energy management performance. On this basis, an optimization objective is constructed to identify clusters exhibiting significant energy management differences during the approach phase. We successfully identify group-scale precursors with energy management issues by applying CMCA to a combination of minority-labeled and majority-unlabeled flights. Comparative experiments show that these precursors have energy levels that deviate from normal flights by 5.83% and 10.93%, respectively, 1000 ft above touchdown, demonstrating the effectiveness of our method. The analysis suggests that poor energy management awareness on the part of pilots could be responsible for these group-scale precursors. Notably, the results obtained using CMCA are comprehensible for Subject Matter Experts, making the method a valuable tool for proactive flight safety management.

Determinants of Sustainable Land Use Change in Agricultural Utilization and Environmental Performance

This paper aims to analyze the determining factors in the use of sustainable agro-industry by implementing renewable energy and environmental management performance. The paper also aims to investigate environmental policies carried out by the local government to minimize land-use conversion of agricultural utilization to non-agricultural functions. This study uses a data driven empirical approach in examining agro-industrial utilization in land-use conversion in Boyolali, Indonesia. The nature of the study was by employing sociology of law to analyze policy aspects in social interactions in environmental performance and sustainability ideas in land-use coversion cases. The results of the study show that the orientation of economic growth by implementing a renewable system and preserving agricultural land is the basis for regional policies to protect agricultural land. The findings also highlight spatial planning as the most important aspect in technical implementation of sustainability pratices in the cases of land-use conversion to non-agricultural functions. The findings would imply the need for optimization and control of agricultural land protection through environmentally-friendly local policies to control land-use conversion and maintain sustainable functions of land. The findings also imply to the importance of environmentally-friendly policies ranging from preparation of regulations, determination of regional spatial plans, environmental permits, and incentives in managing sustainability of environmental performance in land-use policy in local context.

A multi-layer interactive peak-shaving model considering demand response sensitivity

The development of smart grids allows residential customers to participate in demand response (DR) programs to aid power grid management through HEMS (Home Energy Management Systems), but similar electricity consumption behavior among customers based on time-of-use electricity prices may lead to the problem of peak load shift, also known as peak rebounds. This article proposes a multi-level interactive optimization model considering individual sensitivity for DR. The model consists of community energy aggregators (CEAs), which perform as an intermediate processing layer between customers and power grid. Customer terminals perform energy management for home appliances, electric vehicles, energy storage systems, and renewable energy generation. The scheduling problem is decomposed into smaller parallel decision problems that are easier to solve. Renewable generation especially photovoltaic power generation is predicted and used to mitigate the influence of energy generation uncertainty. By introducing numerical responsiveness of customers, the model deals with uncertainty on the subjective level of customers. As indicated in numerical analyses, the model is a good compromise between stochastic optimization depending on idealized probability models and robust optimization sacrificing cost to meet worst case scenarios. The proposed method was compared with existing optimization-based methods for peak shaving. Compared with coordinated load management, our method reduced the peak load and average cost by 13.36% and 18.96%, respectively. Compared with robust optimization, our method achieved similar effect while handling the uncertainty in customers and PV.

It's not just technical - Socio-technical challenges to the efficient operation of commercial office buildings

Facility management companies are the primary agents that mediate between building systems and users. As such, the impact of the socio-technical aspects within facility management companies can play a central role in operational energy management. In this study, we conducted and analyzed individual interviews with 11 building management professionals across two facility management companies. Participants included management, energy analysts, and building operator personnel, collectively working on eight commercial office buildings. Our interviews aimed to answer three research questions: (1) How is improving OE viewed, prioritized, and incentivized within the two FM organizations? (2) How are the current building settings and sequences of operations determined and documented by these FM companies? (3) What tools are used by these FM companies to analyze energy when making operational decisions? Using the results from our interviews and existing challenges identified in the literature, we identify nine primary challenges faced by these organizations and provide nine recommendations aimed at the industry. The recommendations cover organizational challenges, contracts with outside stakeholders, implementing sensors and tools, and the development of future tools. The diversity of the recommendations highlights the need for socio-technical analysis of facilities management companies and contributes to a growing body of this type of research.