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This paper presents an optimal control strategy for a district heating power plant with thermal energy storage. The main goal of the control strategy is to reduce the operation costs of the power plant, by scheduling the boilers, the operation of the thermal energy storage and the curtailment on the loads. The problem is stated as a constrained optimization in the form of a Mixed Integer Linear Program (MILP), embedded on an Model Predictive Control (MPC) framework. Particular attention is paid to modeling of boilers operating constraints, including the outlet water flow temperature, to the energy exchanged with the thermal energy storage and to the operating modes of the power plant layout, including the constraints related to the supply water temperature needed from the network. The results are performed using the data and the layout of the power plant located in the city of Ylivieska, in Finland. The cost analysis performed shows the advantages of using the predictive control strategy.

Worldwide, the penetrations of photovoltaic (PV) and energy storage systems are increased in power systems. Due to the intermittent nature of PVs, these sustainable power systems require efficient managing and prediction techniques to ensure economic and secure operations. In this paper, a comprehensive dynamic economic dispatch (DED) framework is proposed that includes fuel-based generators, PV, and energy storage devices in sustainable power systems, considering various profiles of PV (clear and cloudy). The DED model aims at minimizing the total fuel cost of power generation stations while considering various constraints of generation stations, the power system, PV, and energy storage systems. An improved optimization algorithm is proposed to solve the DED optimization problem for a sustainable power system. In particular, a mutation mechanism is combined with a salp–swarm algorithm (SSA) to enhance the exploitation of the search space so that it provides a better population to get the optimal global solution. In addition, we propose a DED handling strategy that involves the use of PV power and load forecasting models based on deep learning techniques. The improved SSA algorithm is validated by ten benchmark problems and applied to the DED optimization problem for a hybrid power system that includes 40 thermal generators and PV and energy storage systems. The experimental results demonstrate the efficiency of the proposed framework with different penetrations of PV.

Abstract The JET 2019–2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019–2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (α) physics in the coming D–T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D–T benefited from the highest D–D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER.

Abstract The contamination of water bodies by heavy metals is one of the main concerns for environmental protection, due to the intrinsic toxicity and long lifetime of these compounds. Consequently, there is a pressing need to reduce emissions of heavy metals in wastewater, using efficient and cost-effective remediation techniques, which at the same time will not produce toxic residues, e.g. adsorption on activated carbon. The present work deals with the optimal design and operation of adsorption columns for the removal of cadmium and nickel from synthetic aqueous solutions, both in single-compound and binary systems. Thermodynamic experimental tests were carried out on activated carbon samples (GAC), produced starting from a commercial carbon by chemical oxidation with either nitric acid or hydrogen peroxide, to support the dynamic study and to identify the sample with higher adsorption capacity. Subsequently, the dynamic behavior of Cd and Ni adsorption was studied by means of software simulations in order to attain an optimized column design. Several working configurations were explored, investigating the effects of GAC physical and chemical properties, pollutant concentration and flow rate, for a wide application in process operations. Moreover, the intertwining between thermodynamic and dynamic parameters in the performance of a fixed-bed was highlighted. All the transport phenomena that can affect the overall performance, i.e. axial dispersion, external film diffusion and intraparticle mass transport were considered. In particular, for the internal transport, both pore and surface diffusions were taken into account and considered to occur in parallel; moreover the contribution of pollutant surface diffusivity, which is usually neglected in the commonly adopted model because difficult to estimate, was isolated and its influence on the overall adsorption rate was elucidated. The general formulation of the kinetic model allowed estimating the contribution of each adsorption rate controlling parameter and the effect exerted by the main fluid dynamic parameters, hence representing a fundamental tool for the design and optimization of an adsorption column.

Abstract Global warming is predicted to significantly alter species physiology, biotic interactions and thus ecosystem functioning, as a consequence of coexisting species exhibiting a wide range of thermal sensitivities. There is, however, a dearth of research examining warming impacts on natural communities. Here, we used a natural warming experiment in Iceland to investigate the changes in above‐ground terrestrial plant and invertebrate communities along a soil temperature gradient (10°C–30°C). The α‐diversity of plants and invertebrates decreased with increasing soil temperature, driven by decreasing plant species richness and increasing dominance of certain invertebrate species in warmer habitats. There was also greater species turnover in both plant and invertebrate communities with increasing pairwise temperature difference between sites. There was no effect of temperature on percentage cover of vegetation at the community level, driven by contrasting effects at the population level. There was a reduction in the mean body mass and an increase in the total abundance of the invertebrate community, resulting in no overall change in community biomass. There were contrasting effects of temperature on the population abundance of various invertebrate species, which could be explained by differential thermal tolerances and metabolic requirements, or may have been mediated by changes in plant community composition. Our study provides an important baseline from which the effect of changing environmental conditions on terrestrial communities can be tracked. It also contributes to our understanding of why community‐level studies of warming impacts are imperative if we are to disentangle the contrasting thermal responses of individual populations.