An essential challenge in generation scheduling (GS) problems of hydrothermal power systems is the inclusion of adequate modeling of the hydroelectric production function (HPF). The HPF is a nonlinear and nonconvex function that depends on the head and turbined outflow. Although the hydropower plants have multiple generating units (GUs), due to a series of complexities, the most attractive modeling practice is to represent one HPF per plant, i.e., a single function is built for representing the plant generation instead of the generation of each GU. Furthermore, due to the computation time constraints and representation of nonlinearities, the HPF must be given by a piecewise linear (PWL) model. This paper presented some continuous PWL models to include the HPF per plant in GS problems of hydrothermal systems. Depending on the type of application, the framework allows a choice between the concave PWL for HPF modeled with one or two variables and the nonconvex (more accurate) PWL for HPF... [more]

The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total CO2 emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey−Clark−Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs’ aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we ob... [more]

Countries’ emission reduction commitments under the Paris Agreement have significant implications for lifestyles. National planning to meet emission targets is based on modelling and analysis specific to individual countries, whereas global integrated assessment models provide scenario projections in a consistent framework but with less granular output. We contribute a novel methodology for translating global scenarios into lifestyle implications at the national and household levels, which is generalisable to any service or country and versatile to work with any model or scenario. Our 5Ds method post-processes Integrated Assessment Model projections of sectoral energy demand for the global region to derive energy-service-specific lifestyle change at the household level. We illustrate the methodology for two energy services (mobility, heating) in two countries (UK, Sweden), showing how effort to reach zero carbon targets varies between countries and households. Our method creates an ana... [more]

To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed-integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison... [more]

Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the ef... [more]

Time synchronization is one of the most fundamental problems on the internet of things (IoT). The IoT requires low power and an efficient synchronization protocol to minimize power consumption and conserve battery power. This paper introduces an efficient method for time synchronization in the IoT called low-power multi-hop synchronization (LPSRS). It employs a reference node scheduling mechanism to avoid packet collisions and minimize the communication overhead, which has a big impact on power consumption. The performance of LPSRS has been evaluated and compared to previous synchronization methods, HRTS and R-Sync, via real hardware networks and simulations. The results show that LPSRS achieves a better performance in terms of power consumption (transmitted messages). In particular, for a large network of 450 nodes, LPSRS reduced the total number of transmitted messages by 53% and 49% compared to HRTS and R-Sync, respectively.

One of the big concerns when planning the expansion of power distribution systems (PDS) is reliability. This is defined as the ability to continuously meet the load demand of consumers in terms of quantity and quality. In a scenario in which consumers increasingly demand high supply quality, including few interruptions and continuity, it becomes essential to consider reliability indices in models used to plan PDS. The inclusion of reliability in optimization models is a challenge, given the need to estimate failure rates for the network and devices. Such failure rates depend on the specific characteristics of a feeder. In this context, this paper discusses the main reliability indices, followed by a comprehensive survey of the methods and models used to solve the optimal expansion planning of PDS considering reliability criteria. Emphasis is also placed on comparing the main features and contributions of each article, aiming to provide a handy resource for researchers. The comparison i... [more]

Wireless rechargeable sensor networks (WRSN) are utilized in environmental monitoring, traffic video surveillance, medical services, etc. In most existing schemes, WRSNs provide sustainable energy for sensor nodes by employing one or more wireless charging vehicles (WCVs). However, two essential drawbacks, regional limitations and traveling speed limitations, constrain these schemes when applied in hostile and large-scale environments. On the other hand, benefiting from the intrinsic flexibility, high flight speed, low cost, and small size of drones, some works have used drones to charge sensor nodes. However, suffering from limited battery capacities, it is also hard to only use drones in large-scale WRSNs. To overcome the drawbacks of WCVs and drones, we proposed a novelty wireless charging system that deploys WCV, WCV-carried drones, and wireless charging pads (pads) in a large-scale wireless sensor network. Based on this new wireless charging system, we first formulated a pad deplo... [more]

The benefits of networked microgrids in terms of economics and resilience are investigated and validated in this work. Considering the stochastic unintentional islanding conditions and conventional forecast errors of both renewable generation and loads, a two-stage adaptive robust optimization is proposed to minimize the total operating cost of networked microgrids in the worst scenario of the modeled uncertainties. By coordinating the dispatch of distributed energy resources (DERs) and responsive demand among networked microgrids, the total operating cost is minimized, which includes the start-up and shut-down cost of distributed generators (DGs), the operation and maintenance (O&M) cost of DGs, the cost of buying/selling power from/to the utility grid, the degradation cost of energy storage systems (ESSs), and the cost associated with load shedding. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The results of case studies demonstrate... [more]

The penetration of renewable energy sources (RESs) is increasing in modern power systems. However, the uncertainties of RESs pose challenges to distribution system operations, such as RES curtailment. Demand response (DR) and battery energy storage systems (BESSs) are flexible countermeasures for distribution-system operators. In this context, this study proposes an optimization model that considers DR and BESSs and develops a simulation analysis platform representing a medium-sized distribution system with high penetration of RESs. First, BESSs and DR were employed to minimize the total expenses of the distribution system operation, where the BESS model excluding binary state variables was adopted. Second, a simulation platform based on a modified IEEE 123 bus system was developed via MATLAB/Simulink for day-ahead scheduling analysis of the distribution system with a high penetration of RESs. The simulation results indicate the positive effects of DR implementation, BESS deployment, a... [more]

Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (... [more]

Long-term energy scenarios (LTES) have been serving as an important planning tool by a wide range of institutions. This article focuses on how LTES have been used (and also devised in some cases) in the government sector, and specifically how the new challenges and opportunities brought by the aspiration for the clean energy transition change the way that governments use LTES. The information tends to remain tacit, and a gap exists in understanding the way to enhance LTES use and development at the government level. To address this gap, we draw on the experience from national institutions that are leading the improvement in official energy scenario planning to articulate a set of overarching best practices to (i) strengthen LTES development, (ii) effectively use LTES for strategic energy planning and (iii) enhance institutional capacity for LTES-based energy planning, all in the context of new challenges associated with the clean energy transition. We present implementation experience... [more]

China announced a target of achieving carbon neutrality by 2060. As one of the most promising pathways to minimize carbon emissions, the low-carbon electricity supply is of high consideration in China’s future energy planning. The main purpose of this study is to provide a comparative overview of the regional siting potential of various low-carbon power plants in the Yangtze River Delta of China. First, unsuitable zones for power plants are identified and excluded based on national regulations and landscape constraints. Second, we evaluate the spatial siting potential of the seven low-carbon energy power plants by ranking their suitability with geographic information system (GIS)-based hierarchical analysis (AHP). The results revealed that around 78% of the area is suitable for power plant siting. In summary, biomass power plants have high siting potential in over half of the spatial areas. Solar photovoltaic and waste-to-electricity are encouraged to establish in the long-term future.... [more]

Recently, it has been suggested that microgrids (MGs) can improve the resilience of distribution systems. However, predictions about future faults are uncertain. This makes calculating the exact value of the benefits of system resilience enhancement close to impossible at the time of MG planning. Therefore, this paper proposes a framework for MG planning, which focuses on resilience estimation. To consider the uncertainties of future failure events, the proposed method for estimating the resilience utilized the Monte Carlo simulation. In addition, an optimal scenario was estimated using a cost−benefit analysis and constraints on the expected value of resilience enhancement. In the case study, an actual MG installation at D-university was evaluated to obtain the optimal MG planning scenario. The results show that the capacity and installation locations of the distributed generators (DGs) impact the resilience enhancement. The proposed method can effectively derive the optimal MG plannin... [more]

Given the ongoing transformation of the transport sector toward electrification, expansion of the current charging infrastructure is essential to meet future charging demands. The lack of fast-charging infrastructure along highways and motorways is a particular obstacle for long-distance travel with battery electric vehicles (BEVs). In this context, we propose a charging infrastructure allocation model that allocates and sizes fast-charging stations along high-level road networks while minimizing the costs for infrastructure investment. The modeling framework is applied to the Austrian highway and motorway network, and the needed expansion of the current fast-charging infrastructure in place is modeled under different future scenarios for 2030. Within these, the share of BEVs in the car fleet, developments in BEV technology and road traffic load changing in the face of future modal shift effects are altered. In particular, we analyze the change in the requirements for fast-charging inf... [more]

In the original publication [...]

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experime... [more]

Recently, smart energy hubs with hydrogen conversion and storage have received increased attention in the Netherlands. The hydrogen is to be used for vehicle filling stations, industrial processes and heating. The scientific problem addressed in this paper is the proper sizing of capacities for renewable energy generation, hydrogen conversion and storage in relation to a feasible business case for the energy hub while achieving security of supply. Scenario analysis is often used during the early stages of the energy planning process, and for this an easy-to-use analysis model is required. This paper investigates available modelling approaches and develops an algorithmic modelling method which is worked out in Microsoft Excel and offers ease of use for scenario analysis purposes. The model is applied to case study, which leads to important insights such as the expected price of hydrogen and the proper sizing of electrolyser and hydrogen storage for that case. The model is made available... [more]

Electric power systems (EPS) are exposed to disconnections of their elements, such as transmission lines and generation units, due to meteorological factors or electrical failures. Thus, this research proposes a smart methodology for the re-entry of elements that have been disconnected from the EPS due to unforeseen events. This methodology is based on optimal AC power flows (OPF-AC) which allow verifying the state of variables such as voltage, angular deviation, and power (these variables are monitored in normal and fault conditions). The proposed study considers contingencies N-2, N-3, N-4, and N-5, for which the disconnection of transmission lines and generation units are carried out randomly. The analysis of the EPS after the disconnections of the elements is carried out by means of the contingency index, with which the impact that the disconnections of the elements have on the EPS is verified. In this way, the optimal route is generated to restore the elements that went out of ope... [more]

Making cities smart represents a major potential for sustainable development, where both the quality of life and the economy improve. Implementing new and efficient solutions in a smart city involves a large spectrum of uncertainties related to the size and project complexity. Characterization and assessment of the variables uncertainty in planning methodology seem necessary to reach the best decision about the best approach to achieve favorable realization outcomes for planned projects. By including uncertainties in the planning, assessment makes it possible to calculate result uncertainties for all expectations, and project cost-effectiveness. In this way, planning can be improved, if the most important parameters of result uncertainties are identified, better defined, and controlled. This study describes a parameter uncertainty characterization methodology applied on the cost-benefit analysis of smart city development with a case study, focused on smart metering infrastructure. Para... [more]

The article analyzed the state of the global problem of ecological safety of the EU and argued the need to create a methodology for planning technologically integrated projects of the “European Green Deal” (TIP “EGD”) that accounts for the risks to the stakeholders, i.e., the state, project executors, resource suppliers, project managers, and clients. Each of these has an individual set of values that determines the project risk for them. Herein is proposed a taxonomy of three ranks of TIP “EGD” involving agricultural waste, determined by their characteristics, products, and requirements for the stakeholders. The authors point out the need to create tools for quantitative risk assessment for the stakeholders of TIP “EGD” involving agricultural waste and distinguish four groups of risk components with regard to the value of such projects. A model of value risk formation is presented that addresses the risk management of each of these stakeholder values. The need to develop tools (models... [more]

The objective of this research is to establish a multiobjective fractional programming (MOFP) model for supporting the operational management of a combined heat and power (CHP) system. Compared with the traditional operational optimization model of the CHP system, the importance of the energy intensity (i.e., the ratio of energy consumption and energy production) was emphasized in the MOFP model, which is considered as the system objective for replacing the common objective of minimizing the economic cost. This innovative transformation effectively reduces excessive energy consumption, accompanied by improvement in the system revenue. The CHP system of an industrial park in the City of Jinan, China, was used as a study case for demonstration. The obtained results reflected that the combination of two gas turbines (GTs) ensured safe, efficient, and stable output for meeting daily power requirements in various seasons. As for the steam load, during the summer, two heat recovery steam gen... [more]

With the large-scale volatility and uncertainty of the centralized grid connection of wind power, the dimensionality disaster problem of wind farm cluster (WFC) scheduling optimization calculation becomes more and more significant. In view of these challenges, a distributed active power optimal dispatch model for WFC based on the alternating direction multiplier method (ADMM) is proposed, and the analytical description of the distribution characteristics of the active power output of wind farms is introduced into the proposed model. Firstly, based on the wake effect, the Weibull distribution of wind speed is transformed by the impulse function to establish an analytical model of the active output distribution of the wind farm. Secondly, the optimization goal is to minimize the expected sum of the deviations of the dispatch instructions and the output probability density function of each wind farm, constructing a WFC active power optimal dispatch model considering uncertainty. Finally,... [more]

The objective of Poland’s energy policy is to guarantee energy security while enhancing economic competitiveness and energy efficiency, thus minimizing the power sector’s environmental impact and optimizing the use of energy resources in the country. Poland is not the only European country to rely on coal for power generation. Historical factors and large coal deposits act as natural barriers to increasing the share of renewable energy in the Polish power sector. Yet, today, environmental concerns and climate change are prompting many countries to move away from fossil fuels. Renewable energy sources, such as solar and wind energy, are an alternative to traditional energy generated from fossil fuels. However, investors developing solar and wind farms in Poland encounter numerous problems at each stage of the project. These difficulties are associated mainly with the location, technical requirements, infrastructure and formal and legal documents. This study aimed to identify the key fac... [more]

According to the objectives of the Paris Agreement on climate change, the European energy supply must be fully decarbonised by 2050. For the power sector, a massive deployment of decentralised renewable technologies will be required to provide carbon-free electricity. However, other energy-intensive sectors such as gas, heat, transport, and the industrial sectors are more challenging to decarbonise, since they rely mostly on liquid and gaseous fuels. Consequently, exploiting the synergies between energy vectors in an integrated, multi-energy system represents an opportunity for a cost-effective transition towards a carbon-free economy. The objective of this study is to provide insights on the coupling of power and residential heat supply systems in a centralised multi-energy system by developing a linear program that optimises the interactions between energy carriers such as electricity, heat, hydrogen, biomass, and methane to minimise the long-term investments in generation and storag... [more]