The energy sector is undergoing a transformative shift, driven by advancements in Distributed Energy Resources (DERs), the digitization of the energy supply chain and decarbonization policy ...objectives across the world. This paradigm shift has led to the emergence of Local Energy Markets (LEMs), which enable small-scale prosumers to actively participate in the energy market, trade power, and leverage their flexible resources. To ensure the success and acceptance of LEMs, this paper proposes a cooperative game-theoretic approach that fosters prosumer engagement and fair profit allocation. We utilize prospect theory from behavioural economics to examine the decision-making process of prosumers and incorporate their preferences for changes in wealth status. By adopting a cooperative game structure, prosumers can pool their resources, reduce transaction costs, and enhance data utilization. The paper introduces a novel pricing algorithm inspired by prospect theory that incentivizes prosumer participation and accounts for the uncertainty involved in LEM operations. Additionally, a computationally efficient method for profit allocation based on the variation of the Shapley value is proposed to ensure scheme stability. A use case evaluation is conducted on a real-world low-voltage network, demonstrating the effectiveness of the proposed approach in terms of economic efficiency and market characteristics. The results highlight the benefits of the consumer-centric LEM, including improved local trading dynamics, fair profit distribution, and enhanced grid stability. Overall, this research contributes to the design and development of LEMs that prioritize prosumer engagement, community cooperation, financial inclusion and democratization of the energy market.
Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this ...process has also become important for industrial companies, as their production schedules are greatly impacted by energy costs. Although various approaches have been tested with varying degrees of success, this study focuses on predicting day-ahead market (DAM) prices in different European markets and how this directly affects the optimal production scheduling for various industrial loads. We propose a fuzzy-based architecture that incorporates the results of two forecasting algorithms; a random forest (RF) and a long short-term memory (LSTM). To enhance the accuracy of the proposed model for a specific country, electricity market data from neighboring countries are also included. The developed DAM price forecaster can then be utilized by energy-intensive industries to optimize their production processes to reduce energy costs and improve energy-efficiency. Specifically, the tool is important for industries with multi-site production facilities in neighboring countries, which could reschedule the production processes depending on the forecasted electricity market price.
The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has ...brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm’s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.
In this work, we present the design and implementation of an ultra-low latency Deep Reinforcement Learning (DRL) FPGA based accelerator for addressing hard real-time Mixed Integer Programming ...problems. The accelerator exhibits ultra-low latency performance for both training and inference operations, enabled by training-inference parallelism, pipelined training, on-chip weights and replay memory, multi-level replication-based parallelism and DRL algorithmic modifications such as distribution of training over time. The design principles can be extended to support hardware acceleration for other relevant DRL algorithms (embedding the experience replay technique) with hard real time constraints. We evaluate the accuracy of the accelerator in a task offloading and resource allocation problem stemming from a Mobile Edge Computing (MEC/5G) scenario. The design has been implemented on a Xilinx Zynq Ultrascale+ MPSoC ZCU104 evaluation kit using High Level Synthesis. The accelerator achieves near optimal performance and exhibits a 10-fold decrease in training-inference execution latency when compared to a high-end CPU-based implementation.
In photolithographic processes, nanometer-level-precision wavefront-aberration models enable the machine to be able to meet the overlay (OVL) drift and critical dimension (CD) specifications. ...Software control algorithms take as input these models and correct any expected wavefront imperfections before reaching the wafer. In such way, a near-optimal image is exposed on the wafer surface. Optimizing the parameters of these models, however, involves several time costly sensor measurements which reduce the throughput performance of the machine in terms of exposed wafers per hour. In that case, photolithography machines come across the trade-off between throughput and quality. Therefore, one of the most common optimal experimental design (OED) problems in photolithography machines (and not only) is how to choose the minimum amount of sensor measurements that will provide the maximum amount of information. Additionally, each sensor measurement corresponds to a point on the wafer surface and therefore we must measure uniformly around the wafer surface as well. In order to solve this problem, we propose a sensor mark selection algorithm which exploits genetic algorithms. The proposed solution first selects a pool of points that qualify as candidates to be selected in order to meet the uniformity constraint. Then, the point that provides the maximum amount of information, quantified by the Fisher-based criteria of G-, D-, and A-optimality, is selected and added to the measurement scheme. This process, however, is considered “greedy”, and for this reason, genetic algorithms (GA) are exploited to further improve the solution. By repeating in parallel the “greedy” part several times, we obtain an initial population that will be the input to our GA. This meta-heuristic approach outperforms the “greedy” approach significantly. The proposed solution is applied in a real life semiconductors industry use case and achieves interesting industry as well as academical results.
The upward trend of adopting Distributed Energy Resources (DER) reshapes the energy landscape and supports the transition towards a sustainable, carbon-free electricity system. The integration of ...Internet of Things (IoT) in Demand Response (DR) enables the transformation of energy flexibility, originated by electricity consumers/prosumers, into a valuable DER asset, thus placing them at the center of the electricity market. In this paper, it is shown how Local Energy Markets (LEM) act as a catalyst by providing a digital platform where the prosumers’ energy needs and offerings can be efficiently settled locally while minimizing the grid interaction. This paper showcases that the IoT technology, which enables control and coordination of numerous devices, further unleashes the flexibility potential of the distribution grid, offered as an energy service both to the LEM participants as well as the external grid. This is achieved by orchestrating the IoT devices through a Consumer Digital Twin (CDT), which facilitates the optimal adjustment of this flexibility according to the consumers’ thermal comfort level constraints and preferences. An integrated LEM-CDT platform is introduced, which comprises an optimal energy scheduler, accounts for the Renewable Energy System (RES) uncertainty, errors in load forecasting, Day-Ahead Market (DAM) feed in/out the tariff, and a fair price settling mechanism while considering user preferences. The results prove that IoT-enabled consumers’ participation in the energy markets through LEM is flexible, cost-efficient, and adaptive to the consumers’ comfort level while promoting both energy transition goals and social welfare. In particular, the paper showcases that the proposed algorithm increases the profits of LEM participants, lowers the corresponding operating costs, addresses efficiently the stochasticity of both energy demand and generation, and requires minimal computational resources.
Optimizing and predicting the energy consumption of industrial manufacturing can increase its cost efficiency. The interaction of different aspects and components is necessary. An overarching ...framework is currently still missing, and establishing such is the central research approach in this paper. This paper provides an overview of the current demands on the manufacturing industry from the perspective of digitalization and sustainability. On the basis of the developed fundamentals and parameters, a superordinate framework is proposed that allows the modelling and simulation of energy-specific properties on several product and process levels. A detailed description of the individual methods concludes this work and demonstrates their application potential in an industrial context. As a result, this integrated conceptual framework offers the possibility of optimizing the production system, in relation to different energy flexibility criteria.
•Produce DLMP for all phases/nodes of the distribution grid down to feeder level.•Test on an actual large-scale system without approximations and loss of optimality.•Design a DAM and a RTM to enable ...the optimization of offers and bids by DERs.•Implement a MIP SCUC and a SCED integrated with an unbalanced three-phased DPF.•Coordinate DSO/TSO markets in a multi-level hierarchical energy market design.
In this paper we propose a market design for implementing organized nodal electricity markets for the distribution grid. Distribution System Operators (DSOs) are expected to manage a massive penetration of Distributed Energy Resources (DERs), such as Renewable Energy Resources (RES), Electric Vehicles (EVs), Storage and Demand response. A Day Ahead Market (DAM) and a Real-Time Market (RTM) is proposed to optimize and clear offers and bids submitted by the DERs. A Mixed Integer Programming (MIP) Security Constrained Unit Commitment (SCUC) and a Security Constrained Economic Dispatch (SCED) iterates with a detailed asymmetric and unbalanced three-phased Distribution Power Flow (DPF) to enforce all constraints in the distribution grid. The solution produces Distribution Locational Marginal Prices (DLMPs) for all phases at every node of the distribution grid. A general framework for the coordination of the DSO with the ISO/TSO markets is also presented as an integral part of the proposed multi-level energy market design. The proposed new methodology has been applied to an actual large-scale distribution system. Numerous simulation scenarios have been executed to prove the validity of the proposed approach. In this paper a simple small subset of the actual distribution system is selected to illustrate the proposed methodology.
The improvement on the impact of filter concatenation effect on optical signal quality is investigated and discussed for applications in metropolitan optical networks utilizing cost-effective 10-Gb/s ...transmitters. The sources are low-cost conventional directly modulated lasers (DMLs), fabricated for operation at 2.5
Gb/s but modulated at 10
Gb/s. Performance improvement is achieved by using decision-feedback equalization (DFE) at the receiver end. Experimental studies consider both transient and adiabatic chirp dominated DMLs sources with different chirp characteristics. Measurements have been obtained using a recirculating loop set-up and the performance improvement is evaluated in terms of bit-error-rate (BER) versus number of loops.
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