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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.

Intensified agricultural activities with an excessive fertiliser utilisation result in high penetration rates for several chemical compounds through soil to the ground water reservoirs. One of the main components in fertilisers are nitrate ions, which at high concentrations have harmful effects on human health. There is a number of available technologies for nitrate removal, but the majority of them use chemical additives and generate waste that has to be treated before their release to the environment. One promising alternative is the electrodialysis (ED) process. To make the process more sustainable and allow the deployment of such systems to remote areas where limits on electric power consumption may be present, the ED system can be powered by photovoltaic (PV) solar panels. The challenge is to operate the system efficiently under the intermittence of the solar radiation and the variations in the water concentration of nitrates. In this work, a mathematical model describing the removal of nitrate ions, under a variety of meteorological conditions, and for a variable daily demand of drinking water has been developed. Solar radiation and nitrate feed are real data from an area with drinking water contamination problem. This simulation model predicts the removal rate of nitrate ions, the sizing of the solar panels and the capacity of the battery with respect to the battery’s state of charge. Efficient energy management strategies are developed to ensure that the targeted pure water demand in terms of volume and concentration of nitrates is met and the use of non-renewable, externally provided electricity is minimised.

Forecasting imbalance volumes are of great importance for the different actors in electricity markets. From a transmission system operator (TSO) perspective, balancing supply and demand in real-time is one of the main operational tasks to ensure the safe and reliable operation of the power system, while market participants also use forecasting tools to enhance their participation strategy in electricity wholesale markets. Over the last few years, the increasing integration of renewable energy sources into the power system has created additional complexity for the problem of accurately determining the imbalance volume. In the present work, a case study of the Greek balancing market is presented and analysed. Different algorithms and a set of external predictors are adopted both from the market and operational perspective and compared for two different forecasting horizons.

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 reaction of 4,5-dichloro-1,2,3-dithiazolium chloride with 2-(phenylsulfonyl)acetonitrile (1 equiv) in the presence of pyridine (2 equiv) gave S-(3-chloro-5-cyanoisothiazol-4-yl)benzenesulfonothioate and (Z)-2-(4-chloro-5H-1,2,3-dithiazol-5-ylidene)-2-(phenylsulfonyl)acetonitrile in 19% and 23% yield, respectively. The compounds were fully characterized and the mechanistic rationale is proposed for the formation of the benzensulfonate.

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.

The reuse of nutrient-rich waste in order to create green fertilizers in a sustainable and efficient way is a new strategy for the transition to a circular economy. Digestate is produced as a by-product from anaerobic digestion plants in large amounts creating a major management issue both for storage, utilization or disposal. The current work focuses on the development of an innovative, small-scale technology for the processing of digestate from anaerobic digestion plants that use food, animal, agricultural and sludge waste. The technology targets the recovery of fibres and nutrients for the production of high-performance bio-fertiliser products and clean water from digestate.

Phytoremediation is an emerging concept for contaminated soil restoration via the use of resilient plants that can absorb soil contaminants. The harvested contaminated biomass can be thermochemically converted to energy carriers/chemicals, linking soil decontamination with biomass-to-energy and aligning with circular economy principles. Two thermochemical conversion steps of contaminated biomass, both used for contaminated biomass treatment/exploitation, are considered: Supercritical Water Gasification and Fast Pyrolysis. For the former, the vast majority of contaminants are transferred into liquid and gaseous effluents, and thus the application of purification steps is necessary prior to further processing. In Fast Pyrolysis, contaminants are mainly retained in the solid phase, but a part appears in the liquid phase due to fine solids entrainment. Contaminants include heavy metals, particulate matter, and hydrogen sulfide. The purified streams allow the in-process re-use of water for the Super Critical Water Gasification, the sulfur-free catalytic conversion of the fuel-rich gaseous stream of the same process into liquid fuels and recovery of an exploitable bio-oil rich stream from the Fast Pyrolysis. Considering the fundamental importance of purification/decontamination to exploit the aforementioned streams in an integrated context, a review of available such technologies is conducted, and options are shortlisted. Technologies of choice include polymeric-based membrane gas absorption for desulfurization, electrooxidation/electrocoagulation for the liquid product of Supercritical Water Gasification and microfiltration via ceramic membranes for fine solids removal from the Fast Pyrolysis bio-oil. Challenges, risks, and suitable strategies to implement these options in the context of biomass-to-energy conversion are discussed and recommendations are made.

Electrodialysis is a promising electrochemical process for separation and recovery of useful ions from waste waters. The large number of parameters involved in electrodialysis models makes their estimation challenging. This work proposes a systematic sensitivity analysis approach to identify the impact model parameter variations exert on multiple electrodialysis performance indicators, within a wide operating range. This enables the robust mapping of electrodialysis performance toward the direction of maximum variability in the multi-dimensional parametric space and the identification of operating regimes, where the input parameters exhibit the highest sensitivity. Such regimes are used to determine upper and lower limits in the parameter estimation problem. Parameters and ranges exerting insignificant changes on the output electrodialysis indicators are omitted from evaluation, which reduces the associated complexity. The approach is validated against published experimental results obtained for a lead ions removal electrodialysis recirculation batch process. Only four out of nine parameters need to be estimated, while excellent match is observed between experimental and predicted data.