Energy Management Policy Research Articles

Review of Barriers to Effective Implementation of Waste and Energy Management Policies in Ghana: Implications for the Promotion of Waste-to-Energy Technologies

Issues of rising waste generation are calling for proper management and the sustainable control of waste. This study examines waste- and energy-relevant policies and strategies in Ghana and the stakeholders’ perceptions on such policies and strategies. It explores the gaps and challenges in national policy documents to guide the implementation of waste-to-energy projects in Ghana. The approach adopted includes a comprehensive review of relevant policy documents and key informant interviews with selected key stakeholders. Factors such as limited funding, inadequate logistics, expertise and infrastructure, growing population and negative attitudes of general public towards the environment, amongst others, are the concerns identified. Findings from the policy review revealed that capacity to harness energy from waste could be improved through appropriate technologies suitable for Ghana. Adequate institutional framework, stakeholders and mechanisms to explore opportunities to coordinate implementation of various policy strategies and interventions have been established. Streamlining strategies to constitute components to improve governance on waste management, improving financing to ensure sustainable investment in waste-to-energy projects, improving research on waste-to-energy technologies as well as enhancing public interest and education on proper waste management could enhance the implementation of national waste and energy policies for feasible up-scaling of waste-to-energy technologies in Ghana.

Energy-Aware AI-Driven Framework for Edge-Computing-Based IoT Applications

The significant growth in the number of Internet of Things (IoT) devices has given impetus to the idea of edge computing for several applications. In addition, energy harvestable or wireless-powered wearable devices are envisioned to empower the edge intelligence in IoT applications. However, the intermittent energy supply and network connectivity of such devices in scenarios including remote areas and hard-to-reach regions such as in-body applications can limit the performance of edge computing-based IoT applications. Hence, deploying state-of-the-art convolutional neural networks (CNNs) on such energy-constrained devices is not feasible due to their computational cost. Existing model compression methods, such as network pruning and quantization can reduce complexity, but these methods only work for fixed computational or energy requirements, which is not the case for edge devices with an intermittent energy source. In this work, we propose a pruning scheme based on deep reinforcement learning (DRL), which can compress the CNN model adaptively according to the energy dictated by the energy management policy and accuracy requirements for IoT applications. The proposed energy policy uses predictions of energy to be harvested and dictates the amount of energy that can be used by the edge device for deep learning inference. We compare the performance of our proposed approach with existing state-of-the-art CNNs and data sets using different filter-ranking criteria and pruning ratios. We observe that by using DRL-driven pruning, the convolutional layers that consume relatively higher energy are pruned more as compared to their counterparts. Thereby, our approach outperforms existing approaches by reducing energy consumption and maintaining accuracy.

Design and Performance Analysis of RIS-Aided DCSK-WPC System With Energy Buffer

This paper proposes an energy buffer and reconfigurable intelligent surface (RIS) aided differential chaos-shift-keying (DCSK) wireless-powered communication (WPC) system, which includes an access point (AP), a RIS, and a source node equipped with an energy buffer. In such system, the source node first harvests and stores radio-frequency energy from the AP in the downlink, then uses the stored energy to transmit information to the AP in the uplink. A simple time-switching scheme for the proposed system is designed. Two different energy management policies at the source node are considered, i.e., best-effort policy (BEP) and on-off policy (OOP). The bit-error-rate (BER) expressions of the proposed system with BEP and OOP are derived over multipath Rayleigh fading channels. Results demonstrate that the proposed system is able to achieve better BER performance than the conventional DCSK-WPC system and the RIS aided DCSK-WPC buffer-less system, while keeping the non-coherent feature of the DCSK system.

Climate and air pollution impacts of generating biopower from forest management residues in California

California faces crisis conditions on its forested landscapes. A century of aggressive logging and fire suppression in combination with conditions exacerbated by climate change have created an ongoing ecological, economic, and public health emergency. Between commercial harvests on California’s working forestlands and the increasing number of acres the state treats each year for fire risk reduction and carbon sequestration, California forests generate millions of tons of woody residues annually—residues that are typically left or burned in the field. State policymakers have turned to biomass electricity generation as a key market for woody biomass in the hope that it can support sustainable forest management activities while also providing low-carbon renewable electricity. However, open questions surrounding the climate and air pollution performance of electricity generation from woody biomass have made it difficult to determine how best to manage the risks and opportunities posed by forest residues. The California Biomass Residue Emissions Characterization (C-BREC) model offers a spatially-explicit life cycle assessment framework to rigorously and transparently establish the climate and air pollution impacts of biopower from forest residues in California under current conditions. The C-BREC model characterizes the variable emissions from different biomass supply chains as well as the counterfactual emissions from prescribed burn, wildfire, and decay avoided by residue mobilization. We find that the life cycle ‘carbon footprint’ of biopower from woody residues generated by recent forest treatments in California ranges widely—from comparable with solar photovoltaic on the low end to comparable with natural gas on the high end. This variation stems largely from the heterogeneity in the fire and decay conditions these residues would encounter if left in the field, with utilization of residue that would otherwise have been burned in place offering the best climate and air quality performance. California’s energy and forest management policies should account for this variation to ensure desired climate benefits are achieved.

An Offline Closed-Form Optimal Predictive Power Management Strategy for Plug-In Hybrid Electric Vehicles

This article develops an optimal predictive power management strategy (OP-PMS) for plug-in hybrid electric vehicles (PHEVs) to manage the remaining battery level throughout a defined journey. Apart from using the battery before charging it again at the destination, this approach also supports transit through an Ultra Low Emission Zone (ULEZ), where a PHEV needs to operate in pure electric mode to avoid emissions. This type of PHEV power management strategy (PMS) design problem is a noncausal control problem, where the power demand of the remaining driving cycle and the final battery energy level targets influence the current power management action. Rather than solving the whole optimization problem online, this article presents a simplification that leads to a closed-form analytic solution with parameters that can be computed offline. This article makes three key contributions. First, a novel input-linearization method is developed, which converts the OP-PMS into a convex quadratic programming (QP) problem that captures the essential model nonlinearities using an input transformation and a quadratic cost function. Second, the influence of future power demands and final targets on current energy management policy is explicitly quantified from a dynamic optimal control perspective. Third, the noncausal convex QP is approximated with a closed-form analytic solution that is calculated offline. This leads to a real-time implementable OP-PMS with coefficients that can be precalculated and stored in the engine control unit. To demonstrate the viability of this approach, numerical examples are provided based on a generic PHEV model to verify the efficacy of the proposed OP-PMS.

Climate change on extreme winds already affects off-shore wind power availability in Europe

Off-shore wind energy in Europe plays a key role in the transition to renewable energy, and its usage is expected to increase in the next few decades. According to the working regimes of a wind turbine, wind energy production can be disrupted by extreme atmospheric events related to low wind speed below the cut-in wind speed and high wind speed above the cut-out wind speed. The purpose of this work is to estimate the behavior of extreme winds on the European panorama, over the period 1950–2020, in order to investigate the large-scale weather regimes related to them and their impact on off-shore wind energy availability. We detected significant changes in the frequency of high and low extreme wind events, proving that climate change or long-term internal climate variability have already affected the off-shore wind power output. Moreover, the analysis of weather regimes showed that high and low extreme wind events can occur simultaneously over Europe. Our results suggest the necessity to implement efficient European energy management policies, to minimize the deficit in wind power supply.

Smart energy management system framework for population dynamics modelling and suitable energy trajectories identification in islanded micro-grids

In an increasingly electrified and connected world, renewable energy production and robust distribution as well as sobriety paradigm, both for the individual and the society, will most likely play a central role regarding global systems stability. Consequently, while being able to conceive efficient storage systems coupled with robust energy management strategies present significant interests, a number of related studies often consider the human behaviour factor separately. While not decisive in large industrial factories, human demeanor impact cannot be overlooked in residential areas. As such, this work proposes an innovative and flexible dynamic population model, inspired from epidemiological methods, that allows simulation of a vast spectrum of social scenarios. By pairing this formalization with a smart energy management strategy, a complete framework is proposed. In particular, beyond the theoretical identification of sustainable parameters in a wide diversity of configurations, our experiments demonstrate the relevance of reinforcement learning agents as efficient energy management policies. Depending on the scenario, the trained agent enables an increase of the sustainability areas over baseline strategies up to 200%, thus hinting at ultimately softer societal impact.

Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

The large-scale integration of distributed energy resources into the energy industry enables the fast transition to a decarbonized future but raises some potential challenges of insecure and unreliable operations. Multi-energy Microgrids (MEMGs), as localized small multi-energy systems, can effectively integrate a variety of energy components with multiple energy sectors, which have been recently recognized as a valid solution to improve the operational security and reliability. As a result, a massive amount of research has been conducted to investigate MEMG energy management problems, including both model-based optimization and model-free learning approaches. Compared to optimization approaches, reinforcement learning is being widely deployed in MEMG energy management problems owing to its ability to handle highly dynamic and stochastic processes without knowing any system knowledge. However, it is still difficult for conventional model-free reinforcement learning methods to capture the physical constraints of the MEMG model, which may therefore destroy its secure operation. To address this research challenge, this paper proposes a novel safe reinforcement learning method by learning a dynamic security assessment rule to abstract a physical-informed safety layer on top of the conventional model-free reinforcement learning energy management policy, which can respect all the physical constraints through mathematically solving an action correction formulation. In this setting, the secure energy management of the MEMG can be guaranteed for both training and test procedures. Extensive case studies based on two integrated systems (i.e., a small 6-bus power and 7-node gas network, and a large 33-bus power and 20-node gas network) are carried out to verify the superior performance of the proposed physical-informed reinforcement learning method in achieving a cost-effective MEMG energy management performance while respecting all the physical constraints, compared to conventional reinforcement learning and optimization approaches.

Stability and Control for Buck–Boost Converter for Aeronautic Power Management

The need for greener and cleaner aviation has accelerated the transition towards more electric systems on the More Electric Aircraft. One of the key challenges related to the increasing number of electrical devices onboard is the control of bidirectional power converters. In this work, stability analysis and control of a buck–boost converter for aeronautic applications are presented. Firstly, stability of the buck–boost converter in the Lyapunov sense is proven by resorting to input-to-state stability notions. Then, a novel control design based on second order sliding mode control and uniting control, aimed at overcoming the difficulties generated by the nonlinear input gain function of the system not being sign definite, is presented. Extensive and detailed simulations, designed to emulate one of the possible energy management policies onboard a More Electric Aircraft, confirm the correctness of the theoretical analysis both in buck and in boost mode.

Stochastic Guarantees for Adaptive Energy Harvesting Systems

Energy harvesting is increasingly used as a long-term energy supply for the Internet of Things, wireless sensor networks, and cyber-physical systems. However, the challenge of mitigating the variability of energy harvesting sources needs to be addressed before ubiquitous adoption can happen. Otherwise, an unreliable operation of devices with frequent shutdowns during times of energy scarcity would be encountered. One finds probabilistic performance metrics helpful in designing power management solutions for the long-term operation of energy harvesting nodes. These metrics include the probability of battery depletion, expected energy consumption, expected battery level, and similar. This article proposes a stochastic modeling technique and corresponding analysis which can provide such metrics. Our advanced analysis is based on Markov chains. By modeling harvested energy with random variables, new and existing energy management policies are analyzed and compared. We propose an adaptive energy management strategy inspired by mixed-criticality systems. In the proposed strategy, the system can degrade or drop less essential tasks in real time to ensure a graceful degradation of service in adverse harvesting conditions. We compare the proposed energy management approach to several existing alternatives. To this end, we conduct extensive simulations for indoor and outdoor environments, where our strategy matches or outperforms the state of the art. Initial results also validate the high precision of our stochastic model and analysis in comparison to simulations using real-world data.

CAUSALITY AMONG ENERGY PRICE, TRADE OPENNESS AND ECONOMIC GROWTH IN EMERGING COUNTRIES: A PANEL COINTEGRATION ANALYSIS

This paper investigates the possible existence of dynamic causality between energy price, trade openness and economic growth using data of thirty-six emerging economies over the period 1980 to 2014. Panel unit root tests, panel cointegration, fully modified least squares (FMOLS) methods and panel causality tests are used to investigate this relationship. The FMOLS estimation reveals that higher international reserve, trade openness and energy consumption increase economic growth, whereas higher energy price leads to lower GDP growth. The results also indicate that there is a short-run unidirectional panel causality running from energy price and economic growth. Moreover, there is a bidirectional panel causality operated from trade openness and the total energy consumption. The major policy implication based on the general result of the study is that emerging economy should take necessary steps in making energy management and trade openness policies on the basis of the causal relationship of the variables.

A distributed energy management scheme with the extended optimization horizon for Energy Internet

This paper proposes a distributed energy management with the extended optimization horizon for Energy Internet to minimize the system operation cost. The scheme dispatches various of controllable distributed energy sources of the traditional energy generation and battery energy storage devices. Two original contributions make the proposed scheme distinctive from the existing studies. Firstly, both the classical alternating direction method of multipliers (ADMM) and symmetric ADMM are combined in the new scheme to acquire fast and stable energy management policy, which improves the efficiency of energy management decisions. Secondly, an extended optimization horizon is designed based on the data from the previous day to simultaneously minimize the long-term cost and maintain battery state-of-charge. Simulation results from Pecan Street dataset demonstrate the superiority of the proposed method. Compared to existing distributed methods, the convergence time is reduced by almost 80% and the long-term cost is reduced by 47.8%. • A distributed energy management scheme is proposed for Energy Internet. • A hybrid ADMM is proposed for a fast and stable energy management policy. • An extended optimization horizon is designed to minimize the long-term cost. • The efficiency of the proposed scheme is verified through Pecan Street dataset.

A multi-objective energy coordinative and management policy for solid oxide fuel cell using triune brain large-scale multi-agent deep deterministic policy gradient

In this paper, a data-driven multi-objective energy coordinative management policy is proposed in order to enhance the net output power and efficiency of a solid oxide fuel cell (SOFC) and prevent constraint violations. This study focuses on the optimization agent and controller design for a SOFC power system to maintain stable oxygen excess ratio (OER) and fuel utilization (FU) ratio as well as meet the load demand simultaneously. The optimization agent is responsible for output the reference OER and FU, aiming to achieve maximum net output power and operational efficiency as well as dynamic constraint satisfaction times in terms of OER and FU in real time. By applying reference OER and FU settings, the air and hydrogen flow within the SOFC can be effectively controlled by coordination of the air control agent and hydrogen control agent, respectively. In addition, a triune brain large-scale multi-agent deep deterministic policy gradient algorithm (TBL-MADDPG) is proposed. In order to improve the robustness of the proposed policy, the design of TBL-MADDPG entails curriculum learning, imitation learning and a large-scale multi-agent training framework. The performance of this proposed method is verified by the experiment.

Attention and masking embedded ensemble reinforcement learning for smart energy optimization and risk evaluation under uncertainties

Integrating residential-level photovoltaic energy generation and energy storage for the on-grid system is essential to reduce electricity use for residential consumption from the grid. However, reaching a reliable and optimal control policy is highly challenging due to the intrinsic uncertainties in the renewable energy sources and fluctuating demand profile. In this work, we proposed and designed an ensemble deep reinforcement learning (DRL) algorithm combined with risk evaluation to solve the energy optimization problem under uncertainties. Meanwhile, the attention and masking layer, the state-of-the-art natural language processing techniques, were incorporated into the algorithm to handle the issue of hard constraints, which are frequently encountered in the renewable energy optimization problem. To the best of our knowledge, this work is the first attempt to tackle the energy optimization problem under uncertainty using a scenario-based ensemble DRL approach with a risk evaluation. Through a well-designed single household microgrid energy management system, we found that the attention and masking layer played a crucial role in fulfilling the hard constraint. The ensemble DRL with the increased number of agents showed a significantly improved energy management policy leading to ∼75% of the cost reduction compared with those obtained by using conventional DRL with a single agent. The risk evaluation revealed that the current ensemble DRL approach possessed a high-risk/high-profit feature, which could be significantly improved by designing a risk-aware reward function in future investigations.

Real-Time Optimal Energy Management of Multimode Hybrid Electric Powertrain With Online Trainable Asynchronous Advantage Actor–Critic Algorithm

An online updating framework of an energy management system (EMS) for a multimode hybrid electric powertrain is proposed via cooperation between the asynchronous advantage actor–critic (A3C)-based deep reinforcement learning (DRL) agent and the Markov chain model (MCM). In the overall framework, the DRL agent periodically updates the energy management policy. The MCM expedites the policy update process by generating plenty of probable future drive cycles using recent historical driving data and supplying them to the training process. Assisted with the MCM, the proposed A3C-based energy management framework can yield near-optimal policy for any type of unknown drive cycle in the recent future. Two types of unknown drive cycles are chosen to demonstrate the efficacy of the proposed framework. Type I unknown drive cycle is also generated from the same recent historical driving data but was not included in the training dataset. Type II drive cycle is neither known to the framework nor generated from the same historical data. In type I unknown drive cycle, the trained A3C-based EMS achieves 99% of the fuel economy obtained by the global-optimal EMS and 0.12% deviation from charge sustainability. The trained A3C-based EMS consumes 6%–12% more fuel than the global-optimal EMS for type II unknown drive cycles.

Con-Pi: A Distributed Container-Based Edge and Fog Computing Framework

Edge and Fog computing paradigms overcome the limitations of cloud-centric execution for different latency-sensitive Internet of Things (IoT) applications by offering computing resources closer to the data sources. Small single-board computers (SBCs) like Raspberry Pis (RPis) are widely used as computing nodes in both paradigms. These devices are usually equipped with moderate speed processors and provide support for peripheral interfacing and networking, making them well-suited to deal with IoT-driven operations such as data sensing, analysis, and actuation. However, these small Edge devices are constrained in facilitating multi-tenancy and resource sharing. The management of computing and peripheral resources through centralized entities further degrades their performance and service quality significantly. To address these issues, a fully distributed framework, named Con-Pi, is proposed in this work to manage resources at the Edge or Fog environments. Con-Pi exploits the concept of containerization and harnesses Docker containers to run IoT applications as micro-services. %Moreover, Con-Pi operates in a distributed manner across multiple RPis and enables them to share resources. The software system of the proposed framework also provides a scope to integrate different IoT applications, resource and energy management policies for Edge and Fog computing. Its performance is compared with the state-of-the-art frameworks through real-world experiments. The experimental results show that Con-Pi outperforms others in enhancing response time and managing energy usage and computing resources through its distributed offloading model. Further, we have developed an automated pest bird deterrent system using Con-Pi to demonstrate its suitability in developing practical solutions for various IoT-enabled use cases, including smart agriculture.

Forecasting Building Energy Consumption Using Ensemble Empirical Mode Decomposition, Wavelet Transformation, and Long Short-Term Memory Algorithms

A building, a central location of human activities, is equipped with many devices that consume a lot of electricity. Therefore, predicting the energy consumption of a building is essential because it helps the building management to make better energy management policies. Thus, predicting energy consumption of a building is very important, and this study proposes a forecasting framework for energy consumption of a building. The proposed framework combines a decomposition method with a forecasting algorithm. This study applies two decomposition algorithms, namely the empirical mode decomposition and wavelet transformation. Furthermore, it applies the long short term memory algorithm to predict energy consumption. This study applies the proposed framework to predict the energy consumption of 20 buildings. The buildings are located in different time zones and have different functionalities. The experiment results reveal that the best forecasting algorithm applies the long short term memory algorithm with the empirical mode decomposition. In addition to the proposed framework, this research also provides the recommendation of the forecasting model for each building. The result of this study could enrich the study about the building energy forecasting approach. The proposed framework also can be applied to the real case of electricity consumption.

Investigating the relevance of Environmental Kuznets curve hypothesis in Saudi Arabia: towards energy efficiency and minimal carbon dioxide emission.

The Saudi economy is driven by the energy sector which mainly derived from petroleum-based resources. Besides export, the Kingdom’s consumption of this resource showed a significant increase which linearly promoting CO2 emission increment. Therefore, it is essential to model the Kingdom’s energy consumption to estimate the profile of her future energy consumption. This work explores modelling and multi-step-ahead predictions for energy use, gross domestic product (GDP), and CO2 emissions in Saudi Arabia using previous data (1980–2018). The dynamic interrelationship of the variable’s nexus was tested using the Granger causality and cointegration method in the short-run and long-run. In the long-run, the models reveal an inverted U-shape relation between CO2 emissions and GDP, validating Environmental Kuznets curve. When energy consumption is increased by 1%, there will be an increase in CO2 emissions by 0.592% at constant GDP, and when GDP is increased by 1%, there will be an increase in CO2 emissions by 0.282% at constant energy used. CO2 emissions appear to be both energy consumption and income elastic in Saudi Arabia in the long-run equilibrium. Granger causality based on vector error correction method reveals unidirectional causality from income to CO2 emissions, and bidirectional causality from CO2 emissions to energy consumption and vice versa in the short-run. In the long-run, bidirectional causality from income to CO2 emissions and vice versa and unidirectional causality from the used energy to CO2 emissions were observed. Also, there is a bidirectional causality from GDP to energy used and vice versa in the short-run, meaning that GDP and energy consumption are interdependent. Saudi Arabia needs to increase energy infrastructure investments and increase energy efficiency by implementing energy management policies, reducing environmental pollution, and preventing the negative effect on economic growth.Graphical abstract

The Current Status and Future Potential of Biogas Production from Canada’s Organic Fraction Municipal Solid Waste

With the implementation of new policies supporting renewable natural gas production from organic wastes, Canada began replacing traditional disposal methods with highly integrated biogas production strategies. Herein, data from published papers, Canadian Biogas Association, Canada’s national statistical agency, and energy companies’ websites were gathered to gain insight into the current status of anaerobic digestion plants in recovering energy and resource from organic wastes. The availability of materials prepared for recycling by companies and local waste management organizations and existing infrastructures for municipal solid waste management were examined. Governmental incentives and discouragements in Canada and world anaerobic digestion leaders regarding organic fraction municipal solid waste management were comprehensively reviewed to identify the opportunities for developing large-scale anaerobic digestion in Canada. A range of anaerobic digestion facilities, including water resource recovery facilities, standalone digesters, and on-farm digesters throughout Ontario, were compared in terms of digestion type, digester volume, feedstock (s), and electricity capacity to better understand the current role of biogas plants in this province. Finally, technology perspectives, solutions, and roadmaps were discussed to shape the future in terms of organic fraction municipal solid waste management. The findings suggested that the biogas industry growth in Canada relies on provincial energy and waste management policies, advanced technologies for diverting organic waste from landfills, improving biogas yield using existing pretreatment methods, and educating farmers regarding digester operations.

A decision support system for sustainability prioritization of air pollution control technologies in energy and carbon management: Oil & gas industry of Iran

The environmental, economic, social, and technical challenges associated with implementing the air pollution control technologies (APCTs) in oil, gas, and petrochemical industries (OGPIs) necessitate a sustainability prioritization analysis. Environmental issues, economic parameters, social concerns, and technical performances all must assessed and weighted. Accomplishing such a crucial decision with rigorous potential environmental, economic, social, and technical effects needs a robust decision support system (DSS) for guiding the sustainability prioritization process of the technologies. This study purposes in developing a hybrid DSS by integrating the analytical network process (ANP), decision-making trial and evaluation laboratory (DEMATEL), and multi-objective optimization on the basis of ration analysis (MULTIMOORA) methods based on the fuzzy set theory for sustainability prioritization of implementing APCTs. Three cases of the petrochemical plant, gas refinery, and oil refinery in Iran are selected to assess and prioritize the most commonly used of APCTs including four alternatives of Electrostatic precipitators, Fabric filters, and Wet scrubbers, and Cyclones by the proposed DSS. The sustainability ranking of implementing the four technologies sequentially for three cases is obtained. The suggested DSS is feasible for group decision-making based on experts’ opinions and uncertain data. It can assist the stakeholders/decision-makers to attain the energy and carbon management (ECM) policies in three cases and apply to advance the oil & gas supply chains towards the targets of sustainability, green growth, and low-carbon economy. Finally, sensitivity analysis indicates the robustness of the proposed approach for the prioritization order.