Smart Energy is a key element of a Smart City concept and understanding the current state and prospective developments of Smart Energy approaches is essential for the effective and efficient energy supply for the needs of the exponentially growing energy demands of contemporary cities. This review analyzes the inclusion of the Smart Energy agenda in Polish Smart City development plans applying content analysis methodology. The stakeholders’ involvement, spatial dimensions, Smart Energy conceptions, and Smart Energy key sectors were identified as the most commonly referenced Smart Energy agenda components. Stakeholders’ involvement in Smart Energy agendas covers all the crucial key actors—universities, local businesses, and public governance institutions. The spatial dimension components of the Smart Energy agenda comprise the individual, city, regional (sub-regional), country, and international (EU) levels, with the natural dominance of the city’s level. The Smart Energy conceptions co... [more]

In the process of creating a prediction model using artificial intelligence by utilizing a deep neural network, it is of utmost significance to know the amount of insolation that has an absolute effect on the quantity of power generation of a solar cell. To predict the power generation quantity of a solar power plant, a deep neural network requires previously accumulated power generation data of a power plant. However, if there is no equipment to measure solar radiation in the internal facilities of the power plant and if there is no record of the existence of solar radiation in the past data, it is inevitable to obtain the solar radiation information of the nearest point in an effort to accurately predict the quantity of power generation. The site conditions of the power plant are affected by the geographical topography which acts as a stumbling block while anticipating favorable weather conditions. In this paper, we introduce a method to solve these problems and predict the quantity... [more]

As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such forecasting for Thai data. The proposed model uses voting regression (VR), producing forecasts with weighted averages of forecasts from five individual models: three parametric multiple linear regressors and two non-parametric machine-learning models. The regressors are linear regression models with gradient-descent (LR), ordinary least-squares (OLS) estimators, and generalized least-squares auto-regression (GLSAR) models. In contrast, the machine-learning models are decision trees (DT) and random forests (RF). To select the best model variables and hyper-parameters, we used cross-validation (CV) performance instead of the test data performance, which yielded overly good test performance. We compared various validation schemes and found that the Blocked-CV scheme gives the validation error closest to the test error. Using Blocked-CV, th... [more]

In the present paper, we introduce a decision-support framework to (a) classify and prioritize the municipalities of a country based on their suitability to host PV energy projects and (b) pinpoint and evaluate suitable technically and economically viable, as well as environmentally and socially sustainable, sites for PV installation in the most suitable municipalities of a country. The proposed framework is applied in Portugal. It consists of two distinctive stages: ‘Energy Roadmap for PV Deployment’ and ‘PV Site-Selection Analysis and Assessment’. In the first stage, the most and least suitable municipalities for PV deployment in Portugal are identified by analyzing important environmental and technoeconomic PV siting criteria in GIS and applying the TOPSIS method. In the second stage, an integrated PV site-selection assessment is conducted in the Portuguese municipality with the highest suitability index for PV installations. This is achieved by combining a proper GIS siting model w... [more]

The complexity and uncertainty of power sources connected to transmission networks need to be considered. Planners need information on the sustainability and economics of transmission network expansion planning (TNEP). This work presents a newly proposed method for TNEP that considers high-penetration solar energy by using the particle swarm optimization (PSO) algorithm. The power sources, thermal and hydropower plants, and conditions of load were set in the account, including an uncertain power source and solar energy (PV). The optimal sizing and locating of the PV to be connected to the network were determined by the PSO. The PV grid code was set in the account. The new line’s investment cost and equipment was analyzed. The PV cost was considered based on the power loss, and the system’s reliability was improved. The IEEE 118 bus test system and Lao PDR’s system were requested to test the proposed practice. The results demonstrate that the proposed TNEP method is robust and feasible.... [more]

This paper examines the issue of strategic planning of fuel supplies in combined heat and power systems. This is a major challenge in energy modeling because heating-degree day calculation methods only address short-term horizons and are not suitable for the long-term planning of fuel supplies. In this work, a comprehensive method is proposed for strategic fuel supply planning of independent heat producers. The method considers changes in the market dynamics of residential and commercial properties, the annual rate of customer acquisition by the network operator, customer disconnections, as well as the thermal modernization of buildings for estimating the long-term thermal energy demand of an urban area. Moreover, the method develops a mathematical model to minimize production costs, taking into account the technical constraints of the system. The proposed strategic planning tool, in addition to information on the quantities of fuel consumed for heat and electricity production, also pr... [more]

Due to the uneven distribution of water resources, there are many water diversion projects around the world, such as the South-to-North Water Diversion Project in China, especially in some plain areas. To transfer water from low to high areas, large low-head pumps have been widely used. The transition process of the pumping station is mainly caused by the sudden change in the flow velocity and pressure of the fluid in the pipeline of the pumping station system caused by the start-up and shutdown processes. The previous research has mainly been based on the one-dimensional characteristic line method. However, due to the characteristics of the low-lift pumping station, the flow passage is short and irregular, and the calculation results often cannot guarantee the accuracy of the calculation. In addition to some faults in the actual operation process, in some pumping stations, accidents or operation-scheduling faults are caused by transient processes, such as a high degree of water hammer... [more]

The interests of upstream, midstream, downstream companies and consumers in the supply chain are jointly affected by service levels and returns. Improving service levels can increase market demand and improve market position, as well as reduce return rates. But the increase in service level will bring an increase in service cost. How to balance the service cost and return cost through pricing decision, so that the profit of supply chain members can be improved, is the problem studied in this paper. In this paper, we consider the effect of service level of network channel on consumers’ return behavior in the context of manufacturer’s dual-channel supply chain when dual channels provide services at the same time, and discuss the effect of service level and return rate on pricing decision of dual-channel supply chain. It was found that return behavior can stimulate manufacturers to improve service levels and increase overall supply chain profits. The higher the return rate in the network... [more]

The gradual expansion of power transmission networks leads to an increase in short-circuit current (SCC), which has an impact on the secure operation of transmission networks when the SCC exceeds the interrupting capacity of the circuit breakers. In this regard, optimal transmission switching (OTS) is proposed to reduce the short-circuit current while maximizing the loadability with respect to voltage stability. However, the OTS model is a complex combinatorial optimization problem with binary decision variables. To address this problem, this paper employs the deep Q-network (DQN)-based RL algorithm to solve the OTS problem. Case studies on the IEEE 30-bus system and 118-bus system are presented to demonstrate the effectiveness of the proposed method. The numerical results show that the DQN-based agent can select the effective branches at each step and reduce the SCC after implementing the OTS strategies.

Financial costs and energy savings are considered to be more critical on average for computationally intensive workflows, as such workflows which generally require extended execution times, and thus, require efficient energy consumption and entail a high financial cost. Through the effective utilization of scheduled gaps, the total execution time in a workflow can be decreased by placing uncompleted tasks in the gaps through approximate computations. In the current research, a novel approach based on multi-objective optimization is utilized with CloudSim as the underlying simulator in order to evaluate the VM (virtual machine) allocation performance. In this study, we determine the energy consumption, CPU utilization, and number of executed instructions in each scheduling interval for complex VM scheduling solutions to improve the energy efficiency and reduce the execution time. Finally, based on the simulation results and analyses, all of the tested parameters are simulated and evalua... [more]

This paper aims to study the current regulation and governance of wind energy turbines on the high seas and detect regulatory challenges. We focus on the existing regulatory framework to develop marine wind farms in areas beyond national jurisdiction (ABNJ), the nature of wind farms and wind turbines in said areas, and which governance schemes and institutions ought to coordinate and regulate any future marine wind energy development. Our research shows that under public international law, the deployment of wind turbines on (most parts of) the high seas for all States is possible, but many issues still remain, either thinly regulated or unanswered. We inquire where, by whom, and how can marine wind parks be built on the high seas according to public international law and the United Nations Convention on the Law of the Sea (the LOSC). Lastly, we evaluate the possible role of marine spatial planning (MSP) in developing wind energy on the high seas.

Unmanned driving technology has always been one focus of mine smart transportation, which is a crucial component of smart mines. However, the descriptions of both the intelligent transportation system and its industrial application are not comprehensive. In order to have an all-encompassing and comprehensive understanding of both the intelligent coal mine transportation system and its industrial application with the intelligent system, this paper summarizes and analyzes the current research status of unmanned driving technology in mines and the industrial application of current mining transportation vehicles. It begins by outlining the current research state of unmanned driving technology in mines and then assesses the advancement of unmanned driving technology. The unmanned transportation system in mines is also introduced, together with its components for perception, location, path planning, vehicle control, and multi-vehicle scheduling. In addition, each component is described in co... [more]

In recent years, due to the rapidly growing global energy crisis and the ever-increasing prices of energy carriers, more attention has been paid to the energy efficiency of existing buildings, especially in the context of reducing harmful emissions and lowering heating costs. The purpose of this study was to analyse the influence of selected factors on heating costs and air pollution in a cold climate based on the example of a service building located in Bialystok, Poland. The following scenarios were assumed: the implementation of a heating schedule, improvement of the thermal insulation of the building envelope, lowering of the indoor temperature in all rooms, and moving away from a traditional heat source (gas boiler) to renewable energy (heat pump). The results showed that improvements in heat transfer coefficients had the greatest impact on reducing heating costs and that emissions from renewable energy sources depend largely on the national energy mix.

Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the em... [more]

With high penetration of renewable energy sources (RESs), advanced microgrid distribution networks are considered to be promising for covering uncertainties from the generation side with demand response (DR). This paper analyzes the effectiveness of multi-objective optimization in the optimal resource scheduling with consumer fairness under renewable generation uncertainty. The concept of consumer fairness is considered to provide optimal conditions for power gaps and time gaps. At the same time, it is used to mitigate system peak conditions and prevent creating new peaks with the optimal solution. Multi-objective gray wolf optimization (MOGWO) is applied to solve the complexity of three objective functions. Moreover, the best compromise solution (BCS) approach is used to determine the best solution from the Pareto-optimal front. The simulation results show the effectiveness of renewable power uncertainty on the aggregate load profile and operation cost minimization. The results also p... [more]

The high-quality operation of a superheated steam temperature (SST) system is a core fact of the safety, economy, and stability of thermal power units. How to improve the control performance of an SST system under large-varying operating conditions is becoming a research hotspot. To solve this challenge, this paper proposes a proportional integral derivative (PID) control strategy based on integral gain scheduling. Based on the introduction of the SST system and classical model under typical operating conditions, the control difficulties of the SST system are analyzed theoretically. Then, a PID control strategy, based on integral gain scheduling, is introduced for the cascade control structure, and the stability of the proposed control strategy is analyzed by calculating the PID stability region. Finally, the effectiveness of the proposed method is verified under nominal and uncertain conditions, where the proposed method could obtain satisfactory tracking and disturbance rejection con... [more]

The article presents the most commonly used multi-criteria analysis methods for choosing the optimal location for future wind parks. The article makes a comparison of the criteria and restrictions of localisation and an overview of the main legal constraints and prospects in the development of renewable energy sources (RES). Financial assistance from the EU to accelerate the achievement of the required indicators was described. Moreover, restrictions considering environmental, social and noise factors that affect the life of the local population and the perception of the landscape visually are important. Additionally, it includes an option for developing wind energy in the absence of the necessary space for construction. In a new approach for the location of the wind farm, to the investors and another researcher related to the topic of wind turbine foundations, we indicate the most important aspects of wind energy control that should be taken into account in wind farm location proceedi... [more]

In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor’s living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper.

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network’s energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies.

Renewable Energy Communities (RECs) are emerging as an effective concept and model to empower the active participation of citizens in the energy transition, not only as energy consumers but also as promoters of environmentally friendly energy generation solutions, particularly through the use of photovoltaic panels. This paper aims to contribute to the management and optimization of individual and community Distributed Energy Resources (DER). The solution follows a price and source-based REC management program, in which consumers’ day-ahead flexible loads (Flex Offers) are shifted according to electricity generation availability, prices, and personal preferences, to balance the grid and incentivize user participation. The heuristic approach used in the proposed algorithms allows for the optimization of energy resources in a distributed edge-and-fog approach with a low computational overhead. The simulations performed using real-world energy consumption and flexibility data of a REC wit... [more]

Despite their positive effects on the decarbonization of energy systems, renewable energy sources can dramatically influence the short-term scheduling of distributed energy resources (DER) in smart grids due to their intermittent and non-programmable nature. Renewables’ uncertainties need to be properly considered in order to avoid DER operation strategies that may deviate from the optimal ones. This paper presents a comprehensive tool for the scenario generation of solar irradiance profiles by using historical data for a specific location. The tool is particularly useful for creating scenarios in the context of the stochastic operation optimization of DER systems. Making use of the Roulette Wheel mechanism for generating an initial set of scenarios, the tool applies a reduction process based on the Fast-Forward method, which allows the preservation of the most representative ones while reducing the computational efforts in the next potential stochastic optimization phase. From the app... [more]

Pick-and-place operations are basic, and are currently the most common for robots operating in the industry. Massive applications makes it reasonable to ask whether, and to what extent these operations are realised in a way that guarantees rational energy consumption. In many cases, the answer to such a question is neither positive nor known. Therefore, this paper attempts to present a rational and systematic approach to the low-energy pick-and-place operations performed by robots. This paper describes a new approach for the robot’s tool centre point path planning, which enables the minimisation of energy consumption wherein productivity in preserved, and where care is taken for the persistence of the critical mechanical components of the robot cooperating with the autonomous mobile platform. The effectiveness of the described approach has been proven from the results of the theoretical, simulation, experimental and implementation tests carried out using an industrial articulated robot... [more]

Energy policy is becoming a key aspect of the everyday worldwide agenda, and the decisions in this field are directly affecting many aspects, such as energy security, energy supply, and consumer final prices, as well as environmental aspects, among others, and will also affect conditions in the coming years with regard to aspects such as energy resource availability decay, climate change effects, or air contamination. During the last decades, many specific efforts in energy planning research have been carried out by different scientists around the world, but very few of their scientifically based conclusions and recommendations have been transferred into energy planning and energy policy. As a consequence, the energy availability and the environmental situation of the world are worsening; the objectives which aim to achieve a maximum of a 1.5 °C increase are far from being achieved, and many different regions are suffering energy supply disruptions and lack of accessible and secure ene... [more]

Due to the uncertainty of the nodal power caused by the varying renewable energies and the variety of loads, the line power of the distribution network (DN) is uncertainty also. In extreme scenarios, the line power may exceed the loading limits and incur overload violations. In this paper, a risk analysis specifically for overload violations based on the security region of the DN is established. This method takes the N-0 security of the DN as the reference to determine the bidirectional security region and violation distances. The calculation of the probability distribution of the overload violation in the distribution lines is established according to the distribution of node injections of the DN by using the semi-invariant algorithm. By referring to the security boundaries, the optimization model of the anti-violation strategy to minimize the cost of anti-violation is derived, by which the severity of violation risk events is obtained accordingly. Assessment of the risk cost is built... [more]

The integration of renewable resources with distribution networks (DNs) is an effective way to reduce carbon emissions in energy systems. In this paper, an economic and low-carbon-oriented optimal planning solution for the integration of photovoltaic generation (PV) and an energy storage system (ESS) in DNs is proposed. A convolutional neural network (CNN)-based prediction model is adopted to characterize the uncertainties of PV and load demand in advance. Then, taking the lowest total economic cost, the largest carbon emission reduction, and the highest system power supply reliability as the optimization objectives, the optimal distribution network planning model is constructed. The improved multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the optimization model, and the effectiveness of the proposed solution is confirmed through a comparative case study on the IEEE-33 bus system. Simulation results show that the proposed solution can better maintain the... [more]