• Energy Research

Recent regulatory advancements by the European Union have positioned Renewable Energy Communities (RECs) at the forefront of energy policy discussions, reigniting significant interest and debate within the scientific community. However, despite all the developments, RECs still face several challenges that need to be addressed and researched to ensure their development and economic sustainability. One key challenge is the creation of effective and fair energy-sharing mechanisms. Thus, this study proposes a Home Energy Management System (HEMS) and a Two-Stage Collaborative Energy Management Strategy (TCEMS) to manage a REC based on both technical and economic aspects, promoting an efficient operation that meets both individual and collective requirements. This is a cooperative and collaborative strategy in which renewable energy production resources and storage systems are shared in an integrated and equitable manner, ensuring a fair and balanced distribution of renewable energy within the REC. In the strategy’s first stage, a dynamic Key of Repartition (KoR) is employed to adaptively allocate renewable energy production among REC participants. In the second stage, Mixed Binary Integer Programming (MBIP) is used to efficiently manage energy surpluses and deficits, ensuring their optimal allocation across participants storage systems. To evaluate the proposed strategy’s performance, a one-year computational simulation was conducted, followed by an experimental validation in a pilot REC over a 24-hour period. The results confirmed the effectiveness and robustness of the strategy, indicating reduced dependence on the utility power grid and optimized utilization of local energy resources.

The backbone of a conventional electrical power generation system relies on hydro-thermal coordination. Due to its intrinsic complex, large-scale and constrained nature, the feasibility of a direct approach is reduced. With this limitation in mind, decomposition methods, particularly Lagrangian relaxation, constitutes a consolidated choice to “simplify” the problem. Thus, translating a relaxed problem approach indirectly leads to solutions of the primal problem. In turn, the dual problem is solved iteratively, and Lagrange multipliers are updated between each iteration using subgradient methods. However, this class of methods presents a set of sensitive aspects that often require time-consuming tuning tasks or to rely on the dispatchers’ own expertise and experience. Hence, to tackle these shortcomings, a novel Lagrangian multiplier update adaptative algorithm is proposed, with the aim of automatically adjust the step-size used to update Lagrange multipliers, therefore avoiding the need to pre-select a set of parameters. A results comparison is made against two traditionally employed step-size update heuristics, using a real hydrothermal scenario derived from the Portuguese power system. The proposed adaptive algorithm managed to obtain improved performances in terms of the dual problem, thereby reducing the duality gap with the optimal primal problem.

This paper presents a control mechanism for position control of linear switched reluctance machine (LSRM) using flower pollination algorithm (FPA). The control mechanism consists of the proportional-integral-derivative (PID) position controller. Therefore, the problem of determining the optimal values for the gains (proportional, integral and derivative) of the position controller is defined as an optimization problem that aims to obtain the optimal gains by minimizing the integral of time multiplied by absolute error (ITAE) of the position. In order to evaluate the performance of the FPA different operating scenarios were considered and the salp swarm algorithm (SSA) and multi-verse optimizer (MVO) were also implemented for results comparison. The results reveal that position control based on the optimal gains obtained by the FPA provides the smallest error, improving the dynamic performance of the LSRM.

Wind power production is uncertain. The imbalance between power committed and delivered on pool markets produces increased costs on the system, which must be paid by defaulting producers, decreasing their revenues. In order to invert this situation wind producers may submit their bids join with hydro resources, due to their availability. This paper proposes an optimization technique regarding the maximum benefit for both producers, taking into account the wind unpredictability. A cascaded hydro system is considered.

Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer’s disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.

The scenario where the renewable generation penetration is steadily on the rise in an increasingly atomized system, with much of the installed capacity “sitting” on a distribution level, is in clear contrast with the “old paradigm” of a natural oligopoly formed by vertical structures. Thereby, the fading of the classical producer–consumer division to a broader prosumer “concept” is fostered. This crucial transition will tackle environmental harms associated with conventional energy sources, especially in this age where a greater concern regarding sustainability and environmental protection exists. The “smoothness” of this transition from a reliable conventional generation mix to a more volatile and “parti-colored" one will be particularly challenging, given escalating electricity demands arising from transportation electrification and proliferation of demand-response mechanisms. In this foreseeable framework, proper Hybrid Energy Systems sizing, and operation strategies will be crucial to dictate the electric power system’s contribution to the “green” agenda. This paper presents an optimal power dispatch strategy for grid-connected/off-grid hybrid energy systems with storage capabilities. The Short-Term Price Forecast information as an important decision-making tool for market players will guide the cost side dispatch strategy, alongside with the storage availability. Different scenarios were examined to highlight the effectiveness of the proposed approach.

Abstract In the competitive power industry environment, electricity price forecasting is a fundamental task when market participants decide upon bidding strategies. This has led researchers in the last years to intensely search for accurate forecasting methods, contributing to better risk assessment, with significant financial repercussions. This paper presents a hybrid method that combines similar and recent day-based selection, correlation and wavelet analysis in a pre-processing stage. Afterwards a feedforward neural network is used alongside Bat and Scaled Conjugate Gradient Algorithms to improve the traditional neural network learning capability. Another feature is the method’s capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing was applied in a day-ahead framework to historical data pertaining to Spanish and Pennsylvania-New Jersey-Maryland (PJM) electricity markets, revealing positive forecasting results in comparison with other state-of-the-art methods.