Conventional food supply chains are centralized in nature and possess challenges pertaining to a single point of failure, product irregularities, quality compromises, and loss of data. Numerous cases of food fraud, contamination, and adulteration are daily reported from multiple parts of India, suggesting the absolute need for an upgraded decentralized supply chain model. A country such as India, where its biggest strength is its demographic dividend, cannot afford to malnutrition a large population of its children by allowing them to consume contaminated and adulterated dairy products. In view of the gravity of the situation, we propose a blockchain-enabled supply chain platform for the dairy industry. With respect to the supply chain platform, the dairy products of choice include milk, cheese, and butter. Blockchain is one of the fastest growing technologies having widespread acceptance across multiple industry verticals. Blockchain possesses the power to transform traditional supply chains into decentralized, robust, transparent, tamper proof, and sustainable supply chains. The proposed supply chain platform goes beyond the aspect of food traceability and focuses on maintaining the nutritional values of dairy products, identification of adulteration and contamination in dairy products, the increasing economic viability of running a dairy farm, preventing counterfeit dairy products, and enhancing the revenue of the dairy company. The paper collates the mentioned functionalities into four distinct impact dimensions: social, economic, operations, and sustainability. The proposed blockchain-enabled dairy supply chain platform combines the use of smart contracts, quick response code (QR code) technology, and IoT and has the potential to redefine the dairy supply chains on socio-economic, operational, and sustainability parameters.

During recent years the electricity supply chain (electricity production, consumption, distribution, storage, and load management) has changed. In this context a new term appears: the prosumer, that is both a consumer and producer of energy in the same entity. In the paper, an energy management approach of several prosumers aggregated in a coalition, the energy district (ED), and coordinated through a central control entity, the coalition coordinator, is proposed. The aim of the coalition coordinator is to maximize the coalition utility also reducing in this way the reverse energy flows at the point of delivery by an appropriate demand response program. The application on an Italian residential housing area is illustrated. The numerical results demonstrate the effectiveness and attractiveness of the proposed energy management scheme.

This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller for grid-connected doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS). It aims at extracting maximum power from the wind by tracking the maximum power peak regardless of wind speed. The proposed MPPT controller implements an ANFIS approach with a backpropagation algorithm. The rotor speed acts as an input to the controller and torque reference as the controller’s output, which further inputs the rotor side converter’s speed control loop to control the rotor’s actual speed by adjusting the duty ratio for the rotor side converter. The grid partition method generates input membership functions by uniformly partitioning the input variable ranges and creating a single-output Sugeno fuzzy system. The neural network trained the fuzzy input membership according to the inputs and alter the initial membership functions. The simulation results have been validated on a 2 MW wind turbine using the MATLAB/Simulink environment. The controller’s performance is tested under various wind speed circumstances and compared with the performance of a conventional proportional–integral MPPT controller. The simulation study shows that WECS can operate at its optimum power for the proposed controller’s wide range of input wind speed.

The 2018/2001/EU renewable energy directive (RED II) underlined the strategic role of energy communities in the EU transition process towards sustainable and renewable energy. In line with the path traced by RED II, this paper proposes a solution that may help local energy communities in increasing self-consumption. The proposed solution is based on the combination of smart metering and smart charging. A set of smart meters returns the profile of each member of the community with a time resolution of 5 s; the aggregator calculates the community profile and regulates the charging of electric vehicles accordingly. An experimental test is performed on a local community composed of four users, where the first is a consumer with a Nissan Leaf, whereas the remaining three users are prosumers with a photovoltaic generator mounted on the roof of their home. The results of the experimental test show the feasibility of the proposed solution and demonstrate its effectiveness in increasing self-consumption. The paper also calculates the subsidy that the community under investigation would receive if the current Italian incentive policies for renewables were extended to local energy communities; this subsidy is discussed in comparison with the subsidies that the three prosumers individually receive thanks to the net metering mechanism. This paper ends with an economic analysis and calculation of savings on bills when the four users create the local energy community and adopt the proposed combination of smart metering and smart charging.