Participants in the energy market are at greater risk of making decisions due to the nonlinear and volatile characteristics of electricity prices. Accurate short-term electricity price forecasting (EPF) is essential to ensure improved resource allocation, grid stability and enable market participants to manage their decisions efficiently. This study proposes a novel two-stage forecasting framework for day-ahead EPF using time series decomposition methods and hybrid deep learning algorithms. In the first stage, features related to EPF at the next time step are predicted. In this stage, the highest-frequency component extracted via Empirical Mode Decomposition (EMD) is further decomposed using Variational Mode Decomposition (VMD) so as to better capture rapid fluctuations and improve the overall prediction accuracy. Moreover, a decentralized deep learning architecture is designed in which Gated Recurrent Unit (GRU) networks are employed for high-frequency components, while Long Short-term Memory (LSTM) networks are used for the remaining components. In the second stage, EPF is generated using a hybrid LSTM and GRU structure, which incorporates both features estimated in the first stage and historical electricity price data. Finally, hyperparameters of the deep learning models are optimized using Bayesian Optimization to enhance performance. To validate the proposed framework, real market data from the DK1 region of Denmark is used. The proposed hybrid prediction framework is evaluated against both machine learning methods and deep learning-based architectures. Experimental results demonstrate that the proposed method achieves approximately 27.15 % lower RMSE compared to traditional machine learning models, and around 28.24 % lower RMSE compared to LSTM-based models.

This paper proposes a methodology for radial medium-voltage distribution networks topology modeling with the voltage-power sensitivity based on smart meters data. Having different characteristic, partial least squares regression (PLS) and deep neural network (DNN) are respectively used to obtain the sensitivity. The relationship between the sensitivity and the topological position is analyzed, based on which, the topology is identified. The impendence of each line is estimated accordingly by the voltage value and the sensitivity obtained by DNN. The methods presented in this paper do not necessitate any prior topological information or additional data apart from voltage and power readings collected by smart meters. Their efficacy is initially evaluated using the IEEE-33 test case, followed by assessment across various states of the IEEE-69, demonstrating remarkable efficiency and accuracy.

Utilizing energy storage systems (ESSs) to perform multiple grid supporting services is an effective way to rationalize the investment of ESS. It is crucial to choose the matching energy storage technologies (ESTs) for achieving the specific stackable services as well as reducing investment costs and increasing revenue. Therefore, the EST suitable for providing stackable services, which includes energy arbitrage and frequency regulation, is proposed in this paper based on a framework. The framework considers the influence of technical, economical, and lifetime parameters. It mainly consists of technical preselection and economic analysis. At first, the requirements of provided services to ESS are adopted as the hard constraints to select the technically feasible ESTs. For economic analysis, a cost-benefit evaluation model for stackable services is proposed. It can quantitatively describe the influence of ESS parameters on cost and revenue. The analysis is based on the estimated parameters of ESTs in 2022. The load data and frequency regulation data are from the IEEE 33-bus distribution system and the PJM market.

The stability of frequency is at risk with increasing penetration of power electronic converters. In this case, the power grid will lack the moment of inertia to maintain a stable voltage and frequency in the event of a large disturbance. In order to improve the stability of the power grid, traditional grid-following control is needed to be transformed to grid-forming control. This paper analyzes the control structure of grid-following control and grid-forming control. Moreover, a case study is exemplified to compare the performance of two control strategies responding to frequency disturbances. Finally, a simulation model of 15 kW grid-connected converter is built in Matlab/Simulink to discuss the performance of the grid-following and grid-forming converters under different working conditions.

Recently, the decarbonization of the electric power system has led to substantial efforts for designing a pathway toward 100% renewable energy resources (RERs). In this article, we propose a novel operational model for the effective participation of the interconnected microgrids with 100% RERs in the transactive energy market. The novelty of the proposed model is mostly related to the use of transactive energy technology for developing the free energy trading environment for the microgrids with 100% RERs as the local energy-trading market to establish a dynamic energy balance in the system. To capture the intermittencies in the system, a hybrid version of the stochastic programming and information gap decision theory (IGDT) method with the risk-averse and risk-seeker strategies is proposed in the deregulated environment. The proposed model is validated by selecting the modified IEEE 14-bus test system. The results indicate the effectiveness of the proposed model in providing the same percentage of cost-saving for microgrids when they simultaneously participate in the transaction energy market. The cooperative energy interactions of the microgrids in the transactive energy market based on the proposed model lead to 18.34% cost-saving for them in comparison with the base model.

One of the difficulties of mining worldwide is that it must be carried out in remote places without grid connection. Therefore it is important to choose the most profitable and reliable combination of energy sources for electrification. In this paper, different technologies to meet the demand of a mine located in Western Australia are studied. Using HOMER Pro, several viable systems, for the resources considered, are obtained. Using analytic hierarchy process (AHP), the most suitable case is selected for further study. Using Monte-Carlo simulations several scenarios are developed for the study of uncertainties, and a risk-constrained optimization algorithm is implemented to obtain the optimal scheduling, the expected cost and conditional value at risk (CVaR). Numerical results demonstrate that the variations of operation cost and CVaR with the increase in risk aversion factor are not of high magnitude, due to rather low variable operation costs of renewable energy sources. It is shown that the proposed hybrid electrification plan, based on WT, PVs and battery, could not only provide a reliable power generation, but also very low daily operating cost.

The planning stage of any project, could it be for an industry, a commercial or energy supply system, has crucial significance and involves judicious contribution from field experts to decision makers (DM). The objective of this paper is presenting a model for planning of energy sources for microgrid using multi-criteria decision making (MCDM) based on analytic hierarchic process (AHP) approach. For developing a model, an educational institution's electrical energy load demand has been considered as reference. In this assessment, the main-utility grid as the primary source of electricity, alongside conventional sources like diesel generator (DG), gas-based combined heat-and-power (CHP) with absorption chiller to meet cooling demand of facility is taken into account. Moreover, proven and comparatively most environmentally friendly renewable energy sources, such as solar photovoltaic (PV) together with battery energy storage system (BESS) have been taken into account. Moreover, the assessment and evaluation for prioritization of energy sources based on critical criteria or attributes and their associated sub-criteria have been judged to make decision. In this model, most of the critically influencing criteria, such as economic, technical, structural, operational and maintenance, environmental and societal aspects are being focused on. In total, nine alternatives-combinations of grid and other energy source(s)-are identified to form the microgrid. The weight score for each combination of sources is computed for each of the 22 criteria and could be presented DMs to enlist priority of alternatives to choose from.

Small to medium enterprises (SMEs), particularly those whose business is focused on developing innovative produces, are limited by a major bottleneck in the speed of computation in many applications. The recent developments in GPUs have been the marked increase in their versatility in many computational areas. But due to the lack of specialist GPUprogramming skills, the explosion of GPU power has not been fully utilized in general SME applications by inexperienced users. Also, the existing automatic CPU-to-GPU code translators are mainly designed for research purposes with poor user interface design and are hard to use. Little attentions have been paid to the applicability, usability, and learnability of these tools for normal users. In this paper, we present an online automated CPU-to-GPU source translation system (GPSME) for inexperienced users to utilize the GPU capability in accelerating general SME applications. This system designs and implements a directive programming model with a new kernel generation scheme and memory management hierarchy to optimize its performance. A web service interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator. Our experiments with nonexpert GPU users in four SMEs reflect that a GPSME system can efficiently accelerate real-world applications with at least 4× and have a better applicability, usability, and learnability than the existing automatic CPU-to-GPU source translators.

Maritime applications have found in the integration of the electric power system a way to further improve efficiency and reduce the weight of new electric ships. This movement has led scientists to integrate smart management systems to optimize the overall behavior of the grid. In this context, power electronics play a key role in linking the different elements of the power architecture. Moreover, the transition toward a dc distribution, which has already been established in other applications, is being regarded as a promising alternative to ease the integration of renewable sources, batteries, and the ever increasing number of dc loads. In this paper, blackbox models are proposed as a tool to foresee the effect of these complex interactions, overcoming the lack of detailed information about the power converters. Large-signal strategies are proposed in order to consider nonlinearities in the static and dynamic behavior of the converters. An accurate model of the physical layer is essential to allow intelligent systems to take the most out of the system performance. This approach offers the opportunity to study the dynamic response of complex interconnected systems, tune the system-level controllers, design protections, or assess the compliance of the system dynamics with the standards. Experimental results are included in order to validate the proposed method.

This paper proposes a robust optimization framework for energy hub management. It is well known that the operation of energy systems can be negatively affected by uncertain parameters, such as stochastic load demand or generation. In this regard, it is of high significance to propose efficient tools in order to deal with uncertainties and to provide reliable operating conditions. On a broader scale, an energy hub includes diverse energy sources for supplying both electrical load and heating/cooling demands with stochastic behaviors. Therefore, this paper utilizes the Information Gap Decision Theory (IGDT) to tackle this uncertainty as an efficient robust optimization tool with low complexity to ensure the optimal operation of the system according to the priorities of the decision maker entity. The proposed optimization framework is also implemented on a benchmark energy hub which includes different energy sources and evaluated under different working conditions.

In this paper a new method is presented to achieve economic exploitation and proper usage of network capacity by exerting controlling actions over flexible loads and energy storage (ES) equipment. Multi-objective planning for demand response programs (DRP) and battery management policies is carried out by considering energy not supplied (ENS). In order to achieve an optimal scheduling, charge/discharge control for batteries, demand response programs and dispatch of controllable distributed generations (DGs) are also considered. Then, the balanced cost and benefits of participants are evaluated. As a whole, the main objective of this paper is to manage the load and energy storage options in a smart grid to reduce ENS, to minimize overall operation cost and to maximize DG operators' (DGOs) profit. These goals are obtained by considering ENS cost in a multi-objective optimization problem. Distribution company (DisCo) modifies energy cost as a signal for DGO in order to coordinate with each other. So, behavior of DGO is based on modified energy price applied by upstream system considering ENS price.

Strict regulation on emissions of air pollutants imposed by the maritime authorities has led to the introduction of hybrid microgrids to the shipboard power systems (SPSs) which acts toward energy efficient ships with less pollution. A hybrid energy system can include different means of generation such as renewables (e.g., solar PV, wind power) and conventionals (e.g., diesel engines) as well as energy storage systems (ESSs) such as batteries, fuel cells and flywheels. To optimally manage different energy sources in a shipboard microgrid while meeting different technical/environmental constraints, it is necessary to set up an energy management system. This paper provides an overview of hybrid shipboard microgrids and discusses different methods of power and energy management in such systems which are essential for control, monitoring and optimizing the overall system performance in various mission profiles.

Next generation power management at all scales is highly relying on the efficient scheduling and operation of different energy sources to maximize efficiency and utility. The ability to schedule and modulate the energy storage options within energy systems can also lead to more efficient use of the generating units. This optimal planning and operation management strategy becomes increasingly important for off-grid systems that operate independently of the main utility, such as microgrids or power systems on marine vessels. This work extends the principles of optimal planning and economic dispatch problems to shipboard systems where some means of generation and storage are also schedulable. First, the question of whether or how much energy storage to include into the system is addressed. Both the storage power rating in MW and the capacity in MWh are optimized. Then, optimal operating strategy for the proposed plan is derived based on the solution from a mixed-integer nonlinear programming (MINLP) problem. Simulation results showed that including well-sized energy storage options together with optimal operation management of generating units can improve the economic operation of the test system while meeting the system's constraints.

During the past few years, microgrids (MGs) have been becoming more attractive as effective means to integrate different distributed energy resources (DERs). To coordinate active and reactive power sharing among DERs, conventional droop control method is widely used as a decentralized control scheme. However, sharing powers among the sources based on the units' rated capacities is not an optimal solution in terms of economy and efficiency. In this paper, a new adaptive droop-based control strategy is proposed for AC MGs to optimally share MG load between corresponding units. The mentioned control strategy is developed in two levels. The upper control level is a mixed-objective optimization algorithm that provides optimal set-points for power generations considering system's constraints and goals, while the lower control level is responsible for tracking the reference signals coming from the upper level. To demonstrate the effectiveness of the proposed control strategy under different operating scenarios, simulation results in a benchmark MG are also presented.

In this paper, an intelligent energy management system based on energy saving and user's comfort is introduced and applied to a residential smart home as a case study. The proposed multi-objective mixed-integer nonlinear programming (MINLP)-based architecture takes the advantages of several key modeling aspects such as load shifting capability and domestic energy micro-generation characteristics. To demonstrate the efficiency and robustness of the proposed model, several computer simulations are carried out under different operating scenarios with real data and different system constraints. Moreover, the superior performance of the proposed energy management system is shown in comparison with the conventional models. The numerical results also indicate that through wise management of demand and generation sides, there is a possibility to reduce domestic energy use and improve the user's satisfaction degree.