Energy Management Policy Research Articles

Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach

In the context of the expanding diversity of energy demands, an increasing number of heterogeneous Multi-energy Microgrids (MEMGs) are engaging in the collaborative framework of the Multi-energy Multi-microgrid System (MEMMG). However, following this trend, the existing centralized Integrated Energy Management System (IEMS) control strategy is unreliable for future energy systems, characterized by more complex optimization control and a flexible system structure. This paper introduces a hierarchical Multi-agent Deep Reinforcement Learning (HMADRL) approach for distributed IEMS in MEMMG. Firstly, by employing a hierarchical approach, this method simplifies control complexity by segmenting the overarching control challenge into tasks of collaborative planning and action control, which are distributed across varied multi-agent scenes. Considering both macro and microeconomic factors, alongside carbon emissions, the optimal operation of MEMMG is realized through a bottom-up edge multi-agent control approach, in contrast to traditional top-down centralized methods. Secondly, in the phase of the inter-MEMG collaborative strategy, the Centralized Training Decentralized Execution (CTDE) framework is adopted to overcome the problems of unstable training environments and large-scale agent training, and each heterogeneous microgrid can develop local strategies independently with the assurance that their internal data will not be overly exposed. Thirdly, within each MEMG, the Trust-Region (TR) model is introduced for multi-agent action control, adeptly addressing the effects of mutual exclusion in decision-making time series. Simultaneously, an initialized Hot Experience Pool (HEP) is implemented, efficiently reducing exploration in complex, high-dimensional spaces. Finally, the off-time agent model is integrated into the HMADRL environment and undergoes secondary training based on real interactions, thereby deriving the optimal energy management policy. The proposed method markedly reduces reliance on exact physical modeling systems. Comparative simulations validate the proposed control scheme’s efficacy.

A comparison on the critical success factors of MSW and RDF power plant development in Thailand: Using an interpretive structural modelling and cross-impact matrix multiplication applied to classification analysis

Waste-to-energy (WtE) power plants, supplied mainly with municipal solid waste (MSW) and refuse-derived fuel (RDF), which convert waste into electricity, have emerged as a solution to Thailand's waste management problems. This study focused on identifying and studying the critical success factors (CSFs) that influence the success of MSW and RDF power plants in Thailand. This study employed interpretive structural Modelling and cross-impact matrix multiplication applied to a classification analysis to evaluate the impact of these CSFs on the development of WtE projects. The results showed that, for MSW, most CSFs were related to energy and waste management policies, followed by waste quality for electricity generation. In addition, strong financial resources and appropriate power plant locations are important for MSW management success. Conversely, for RDF, most CSFs were sufficient waste quality for electricity generation and performed well according to licensing conditions. In this study, high-level CSFs indicated that these factors were crucial for MSW and RDF development. CSFs differ based on specific technologies and regulations. However, sufficient waste quality (heating value and moisture content) is a common CSF in the MSW and RDF technologies. This study provides valuable insights into the CSFs that affect the development of WtE. Understanding and addressing these CSFs is essential for the development and operation of WtE power plants in Thailand and other countries with similar conditions. Thus, policy-makers and other stakeholders can make informed decisions to ensure the success of WtE projects.

The economics of home energy usage: Insights from urban economy

Recent regional investigations in the United States have revealed a thought-provoking perspective: household electricity consumption may act as an inferior commodity, displaying a negative correlation with wealth. This finding challenges the outcomes of various other econometric analyses and underscores the importance of scrutinizing power consumption patterns across different regional service areas. While it is commonly believed that home electricity usage decreases as income rises, this may not always hold true universally. This study focuses on power usage in Seattle, Washington households, a prominent urban economy in the Pacific Northwest. Employing dynamic error correction modeling techniques, the research demonstrates statistically significant fluctuations in domestic power usage attributed to variations in actual value, actual revenue, and cold weather. In the immediate future, energy for homes in this urban economy resembles any other commodity. However, investing in electricity for homes in Seattle may not be advisable in the long run. Home power usage in Seattle declines with each percentage point increase in actual per capita income over 1.2 %. This finding highlights the need for careful consideration and strategic planning in energy management policies for urban economies like Seattle.

The interconnectedness and spillover effects among economic uncertainty, energy-related risks and sovereign risk in BRICS economies

ABSTRACT Emerging economies experiencing energy-intensive growth face increased energy price volatility, which could fuel macroeconomic instability with possible implications on their debt repayment capacity. This study implements a quantile vector autoregressive model on a dataset collected between April 2011 and July 2024 to examine the interplay among economic uncertainty, energy-related risks and sovereign risk in BRICS economies. The empirical results show these risk factors exhibit strong interconnectedness under low- and high-risk profiles. Under a low-risk profile, energy risks are transmitters in all countries except Russia and India. Sovereign risk receives shocks in Brazil, India and South Africa but transmits shocks in Russia and China, whereas economic risk is a shock transmitter in South Africa and India and plays the receiver role in Brazil, Russia and China. In high-risk conditions, economic risk is a shock transmitter in Russia and India but receives shocks in Brazil, China, and South Africa; Sovereign risk receives shocks in all countries except China while energy risk is a transmitter across countries except Russia. Therefore, the dynamics of the studied risks are driven by individual country heterogeneities including risk profile, economic structure, market conditions and energy trade status. Policymakers should combine energy, economic and debt management policies to promote economic stability and build crisis resilience. In conclusion, debt management and investment in renewable energy are alternative policy instruments to promote creditworthiness in emerging countries.

Effects of green policies on sustainability of local economies in Africa

This article conducts a thorough analysis of the potential impacts of environmentally friendly policies on the long-term viability of renewable energy and waste management policies on the economics of local regions within the African Continental Free Trade Area. The research examines how these policies intersect with the development of intra-African enterprise and local employment rates. The study employs a qualitative methodology, utilising an exploratory research design to investigate African economic performance metrics. It also uses qualitative data to gain a deeper understanding of the subtle implications of policies. The research evaluates the economic consequences of these policies in African contexts by utilising a range of secondary sources, such as governmental reports, peer-reviewed studies, and policy evaluations. Initial results indicate that although green policies have resulted in substantial environmental advantages, their influence on intra-African economies is intricate and diverse. The report asserts that comprehending these dynamics is crucial for policymakers, practitioners, and researchers who have an interest in fostering sustainable development and inclusive economic growth in Africa. The study suggests the adoption of environmentally friendly policies that take into account the economic conditions inside Africa and aim to achieve a local economy that is both environmentally sustainable and inclusive.

Simulation Study on Student Residential Energy Use Behaviors: A Case Study of University Dormitories in Sichuan, China

Studying the energy use behavior of occupants is crucial for accurately predicting building energy consumption. However, few studies have considered the impact of occupant behaviors on energy consumption in university dormitories. The objective of this study is to establish an agent-based model of energy consumption for university dormitories based on energy use behavior. The dormitories of a typical university in Sichuan, China, were subdivided into three clusters using a two-step cluster analysis. Subsequently, the energy use behaviors of occupants in each type of dormitory were characterized to establish a stochastic energy use behavior model. On the basis of the above, NETLOGO was used to construct an agent-based model for dormitories’ energy consumption to dynamically simulate energy use behavior. The accuracy of the model was verified by comparing the simulated values with the measured data. Finally, a building-energy-friendly retrofit scheme was proposed, and it was found that the optimized dormitory reduced energy consumption by 16.07%. Therefore, the results can provide information support for energy-saving decisions during the early design and retrofit phases of buildings. With the popularity of centralized supply, the research methodology may provide an extensive reference for energy management policies and sustainable strategies in the building sector.

SWOAM: Swarm optimized agents for energy management in grid-interactive connected buildings

With increasing complexity of smart city energy systems and rising energy demand, effective energy management solutions are crucial. Buildings now incorporate renewable energy sources and battery storage for efficient energy utilization, making optimal control strategies important. Compared to rule-based controllers and model-based methods, swarm and evolutionary algorithms have the advantages of providing cost-effective, stable, and scalable alternatives. However, their potential in data-rich environments and multi-building energy systems remains underexplored. This research bridges this gap by demonstrating the cooperative capabilities of population-based optimization agents for efficient energy management. Specifically, novel algorithm and framework, named Swarm Optimized Agents for Sequential Decision Making (SWOAM) is proposed. It combines an online k-means classifier and a swarm optimizer for optimal control of multi-building energy systems. By designing an online K-means learning strategy and an agent-based sequential episodic sampling approach, it is feasible to train a swarm of agents find an optimal energy management policy for buildings in a district. For training and evaluation, we use the standardized CityLearn building energy management environment. The agents are evaluated on three key metrics: electricity cost, carbon dioxide emissions and grid ramping. SWOAM delivers state-of-the-art performance and outperforms modern reinforcement learning and rule-based controllers.

Deep learning for prediction of energy consumption: an applied use case in an office building

Abstract Non-residential buildings are responsible for more than a third of global energy consumption. Estimating building energy consumption is the first step towards identifying inefficiencies and optimizing energy management policies. This paper presents a study of Deep Learning techniques for time series analysis applied to building energy prediction with real environments. We collected multisource sensor data from an actual office building under normal operating conditions, pre-processed them, and performed a comprehensive evaluation of the accuracy of feed-forward and recurrent neural networks to predict energy consumption. The results show that memory-based architectures (LSTMs) perform better than stateless ones (MLPs) even without data aggregation (CNNs), although the lack of ample usable data in this type of problem avoids making the most of recent techniques such as sequence-to-sequence (Seq2Seq). Graphical abstract

Optimal household appliances scheduling for smart energy management considering inclining block rate tariff and net-metering system

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

The “energy rebound effect” within the framework of environmental sustainability

AbstractThe energy rebound effect, characterized by increasing energy use despite enhanced efficiency, has emerged as a complex interdisciplinary topic in the literature, reflecting its significance in both production and consumption sustainability. Recognizing the pivotal role of academic research in shaping energy management strategies and policies, this study conducted a comprehensive bibliometric review, accessing 530 articles from major citation indexes. This analysis provides insights into the evolution of the energy rebound field and potential future directions. Understanding the existing literature on the rebound effect serves as a valuable foundation for forthcoming research endeavors, addressing the intricate challenge of reconciling energy efficiency with sustainable energy consumption and production. The review explores the extensive literature on the energy rebound effect, highlighting a predominant focus on residential (e.g., cooling, heating, lighting, transportation) and industrial energy consumption in the majority of studies. Empirical applications primarily emphasize the measurement of the energy rebound effect, with a notable trend of increasing research concentration in China in recent years. Additionally, there is a growing body of economy‐wide analyses in this field. However, the literature exhibits complexity in both the analyses conducted and the methods employed for measuring the rebound effect. Research gaps are apparent, numerous studies tend to concentrate solely on the rebound effect at the overall energy consumption level, neglecting variations in energy types and the behavioral patterns of economic actors. Furthermore, despite consistent findings of a higher rebound effect in developing nations, there is a noticeable lack of scholarly literature addressing this issue about these countries.This article is categorized under: Sustainable Energy > Energy Efficiency Human and Social Dimensions > Behavioral Science Policy and Economics > Governance and Regulation

Precautionary Principle: Perumusan Kebijakan Pengelolaan Energi Baru Terbarukan

The transition to new renewable energy not only produces energy that is relatively more environmentally friendly, but there are other consequences on the environment. In this case, policy makers must be careful in making the right decision regarding a particular product or legal product, because the EBT transition has the potential to endanger society as a whole, but at the same time these dangers are not widely understood until there is scientific evidence. This article examines efforts in formulating new and renewable energy management policies with an emphasis on precautionary principles to realize sustainable national legal development. This research uses a doctrinal approach or also called a normative legal approach with descriptive analysis methods. This research shows that the energy transition policy offered by the government does not touch directly on the environmental dimensions that have been detrimental to society. On the other hand, Indonesia, which is new to the concept of EBT, needs to strive to gain trust from its people, which can occur if the system offered is safe. This is done with an established legal umbrella so that the precautionary principle must be the breath in making this EBT regulation.

Improving Demand‐Side Energy Management With Energy Advisor Using Machine Learning

The prediction of consumers’ electricity consumption is an important demand‐side energy management technique for adaptable dynamic tariff pricing. In this regard, historical consumption patterns play an important role in accurately predicting the future energy consumption. In this paper, we propose a novel system based on machine learning (ML) algorithms to accurately predict not only the future energy consumption based on historical data and various environmental factors but also energy‐saving recommendations for the consumers. The proposed system is evaluated on real‐world energy consumption data, and the results show that random forest (RF) more accurately predicts energy consumption with 98% R‐squared value and 0.51 root mean square error, whereas decision tree (DT) is best fit for personalized energy recommendations resulting in 85% R‐squared and 0.14 root mean square error. The implementation of this system will greatly assist utility companies in making precise future energy consumption forecasts to formulate demand‐side energy management policies which are cost‐effective for both consumers and utility companies.

Development of a Management Framework for Energy Efficiency for Sustainability of Hotels in Jeddah, Saudi Arabia Using Structural Equation Modelling

The paper examined the utilized critical factors that affect hotel building sustainability in Jeddah, Saudi Arabia and developed a framework to identify the suitable operational and technical for improving the energy performance of hotel constructions. The mixed methods for collecting energy used data from twenty-four established hotel buildings have been adopted in Jeddah, Saudi Arabia. The survey method was applied in the forms of questionnaires to collect data for the research. The surveying work was carried out on a population of hotel buildings employees with various academic qualification levels and age including engineers, developers, and managers. Structural equation modeling, analysis based on the validation survey, was used to examine the dependencies and interdependencies of the critical factors. The IBM, SPSS 22 and AMOS 23 were used on the basis of maximum likelihood estimates for exploratory factor analysis and confirmatory factor analysis resulting in validated measurement and structural models. This study utilized seventeen critical factors influencing building energy performance in Jeddah, Saudi Arabia. The model identified energy performance building indicators; and established causality as standard metrics for building energy efficiency evaluation and benchmark between building energy performance, management policy, operations, and strategic driver. This recognized strong interdependent relations in the template between the four constructs. The stronger results were: strategic drivers on the energy performance model and management policy on strategic drivers highlighting the importance of energy-saving constructs.

Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model

In the building field, campus buildings are a building group with great energy-saving potential due to a lack of reasonable energy management policies. The accurate prediction of power energy usage is the basis for energy management. To address this issue, this study proposes a novel combined forecasting model based on clustering results, which can achieve a short-time prediction of daily electricity based on a campus building's electricity data over the past 15 days. Considering the diversity of campus buildings in energy consumption and functional aspects, the selected campus buildings are firstly classified into three categories using K-Means clustering in terms of their daily power consumption. Compared with the mainstream building energy consumption prediction models, i.e., LSTM and SVR, the results show that the combined forecast model is superior to other models. Furthermore, an average percentage fluctuation (APF) index is found to be close to the MAPE, which can reflect the prediction accuracy in advance.

A data-driven evidential regression model for building hourly energy consumption prediction with feature selection and parameters learning

Building energy consumption prediction is critical for building energy management and energy policy formulation, and its inherent uncertainty can significantly affect the utilization of current energy market benefits for market participants. To capture the uncertainty of energy consumption and enhance the predictive capability of the model, in this study, a data-driven evidential regression (EVREG) model with integrated feature selection function is proposed based on Dempster–Shafer theory and mutual information, which can perform point prediction and interval prediction for building hourly energy consumption to describe its fluctuation and uncertainty. Different from the traditional EVREG model, this method enables simultaneous feature selection and model parameters learning instead of treating feature selection as a separate data pre-processing step. Specifically, an evaluation function is defined to describe the significance of a candidate feature, taking into account the predictive power of regression model and the redundancy between the candidate feature and already selected features. According to a search strategy, features with high significance are selected to minimize the objective function. A real dataset from a commercial building is used to evaluate the performance of the proposed method. The results demonstrate that the proposed method can select fewer features while achieving better prediction performance compared to traditional feature selection methods used as data preprocessing. The proposed method also achieves better or comparable performance compared to commonly applied point prediction and interval prediction methods.

Uncertainty-aware Energy Harvest Prediction and Management for IoT Devices

Internet of things (IoT) devices are popular in several high-impact applications such as mobile healthcare and digital agriculture. However, IoT devices have limited operating lifetime due to their small form factor. Harvesting energy from ambient sources is an effective method to supplement the battery. Energy harvesting necessitates development of energy management policies to manage the harvested energy. Designing optimal policies for energy management is challenging for two key reasons: (1) ambient energy sources are highly stochastic; therefore, energy management policies must consider the associated uncertainty; (2) energy management policies must consider future energy availability while making decisions to ensure that sufficient energy is available when there is no ambient energy. Prior approaches typically consider energy in the immediate future (e.g., 1 hour) and do not account for the uncertainty in future energy harvest. This article proposes novel machine learning and dynamic optimization-based approaches to handle the two challenges. Specifically, we first develop a novel set of features and use it in a low-power neural network architecture to predict future energy availability and uncertainty. The energy predictions and uncertainty are used in a dynamic optimization algorithm to optimally allocate the harvested energy. Experiments on solar energy data over 5 years from Golden, Colorado, show that the proposed energy prediction model achieves 3.4 J mean absolute error while having a coverage of 80%. Moreover, our energy management algorithm provides energy allocations that are within 2.5 J of an optimal Oracle with 2.65 mJ to 36.54 mJ of energy overhead.

The Role of Technology in Sustainable Energy Management Policy and Policy Practices in South Africa

The Role of Technology in Sustainable Energy Management Policy and Policy Practices in South Africa

Urgensi Pengembangan Kebijakan Energi Baru dan Terbarukan Dalam Rangka Ketahanan Energi Nasional

The existence of the use of new energy and renewable energy until now has not been optimal. This study aims to formulate policy concepts for the management and utilization of new and renewable energy in the framework of national energy security. The research method used is normative legal research using a statute approach. The data collection technique uses primary data, namely a number of laws and regulations related to energy and natural resources and secondary data, namely encyclopedias, news in print media, and websites. Then it is analyzed qualitatively, namely legal norms in laws and regulations using deductive thinking logic. The results show that new energy and renewable energy management policies to maintain national energy security need to use legal protection instruments for the interests of private property, common property and state property so that it is necessary to elaborate on the concept of control rights over natural resources by the state regulated in laws and regulations in Indonesia.

Application of an intelligent method for hydrogen-based energy hub in multiple energy markets

Use of different energy carriers together, known as an energy hub, has been a hot topic of research. The hub manager manages the energy in the hub and supplies the energy demanded by the consumers. Herein, a multi-carrier energy storage system comprising a thermal energy storage system, ice storage system, and hydrogen storage system is considered for a multi-carrier energy system to exploit their economic benefits. The proposed combined cooling, heating and electricity microgrid not only participates in the electricity, gas and heat market to meet the needs of electricity, heating and cooling, but also can use the new hydrogen power technology used in the hydrogen storage system to increase the efficiency of the whole system participate in the market. In addition, a multi-energy demand response model is used as a new concept of demand response on electrical and thermal loads, which provides more options for multi-energy end users in energy management policies. To cover the uncertainty of the existence of different energy sources in the used model and the price of electricity, a new method has been used. The LHS sampling is used to develop diverse scenarios, and the backward scenario reduction method is adopted to decrease the number of scenarios. To solve the objective function and optimization, the metaheuristic whale algorithm improved by a local search algorithm is utilized. The main goal is to reduce the operation costs of the multi-energy carrier system. The obtained results show that the hydrogen storage utilization and multi-energy demand response can reduce the system operation cost by 6% for the studied experimental system. Furthermore, as the uncertainty budget increased, the total cost of operations increased by 10%. This is because the level of planning conservatism increases with each increase in the uncertainty budget.

Group Identities, Value Orientations, and Public Preferences for Energy and Water Resource Management Policy Approaches in the American West

Earlier research suggests social identity’s association with environmental attitudes and behaviors and identifies several social-identity-based strategies that can potentially foster pro-environment behaviors. This paper examines whether and how social identities are associated with support for pro-environment policies. Using our survey of 1,804 randomly selected respondents, we seek to analyze how group identities are associated with demographic characteristics and value orientations of citizens in the American West, and how these group identities are associated with support for policies aimed at resource development (supply expansion) versus conservation (demand reduction) of energy and water resources. Findings from cluster analysis and binary logistic regression models indicate a significant association between social identity and policy preferences. Respondents who strongly identify themselves as environmentalists, conservationists, and wildlife advocates are more likely to support conservation policies and less likely to support resource development policies compared to those who strongly identify as hunters, fishers, and property rights activists.