Buildings are becoming smarter through the deployment of intelligent and energy-efficient technologies that reduce energy consumption while making buildings easier to manage and operate. Demand for lighting systems, efficient heating, ventilation and air conditioning (HVAC), as well as other types of hardware such as controls and sub-meters, is growing rapidly that needs more attention in terms of the energy efficiency. Moreover, the energy management of micro-generation or poly-generation system has always been very complex. Keeping the balance between demand and production is a challenging task for researchers. This paper investigates a simple yet very effective and intelligent demand response algorithm to mitigate the intermittency problem, providing an uninterrupted power supply to the users. The developed algorithm is fully capable of balancing the consumption and the production by controlling the user pre-defined load patterns that is based on the state of charge (SOC) of the storage system. The algorithm is tested on a microgrid system that consists of different loads, six photovoltaic (PV) solar panels, a small wind turbine system, and a set of lead-acid battery banks. The experimental result shows the maximum use of renewable energy while reducing the peak demand, cost of end-users and low or no carbon emissions. To achieve a significant reduction in energy consumption as well as to keep the balance between production and demand, this algorithm can be implemented for the efficient use of intermittent renewable energy sources.

This paper studies the method to theoretically calculate the power loss, maximum switching frequency, and thermal resistance for a grid-connected multilevel converter. Especially, the commercial IGBT modules were selected to demonstrate the calculation procedures, which provides a useful tool for the selection of devices only based on the data sheet to satisfy the design requirement for a multilevel converter.

It is expected that the conventional micro solar inverters can be revolutionized as a result of further investigation of the emerging Internet of Things (IoT) technologies and cloud computing services by providing continuous monitoring, data exchanging, and optimal operation in smart grid environments. However, there are imminent concerns and challenges regarding cybersecurity from cyber-attacks since the inverters through the IoT devices are always connected to networks such as Internet. The paper explores how blockchain technology could be used for potentially ensure communication and data security of the IoT -enabled micro solar inverters.

Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is evaluated through a proposed method of levelized cost of energy (LCOE) for clustered generation portfolios. Effective renewable economics is applied to assess resource adequacy, annual carbon emissions, renewable capacity factor, over generation, and cost to build between thermal dispatch scenarios with incremental increases in VRE penetration. Solar PV and wind generation temporally complement one another in the region studied, and the combination of the two is beneficial to renewable energy integration. Furthermore, replacing older coal units with cleaner and agile natural gas units increases renewable hosting capacity and provides further pathways to decarbonization. Minute-based chronological simulations enable the assessment of renewable effectiveness related to weather-related variability and of complementary technologies, including energy storage for which a sizing procedure is proposed. The generally applicable methods are regionally exemplified for Kentucky, USA, including eight scenarios with four major year-long simulated case studies and 176 subcases using high performance computing (HPC) systems.

Recently, there is increasing interest in using behind-the-meter (BTM) solar systems for grid services. However, providing visibility and operational situational awareness of BTM solar systems mainly operated by small-scale solar inverters is challenging due to the requirement of relatively expansive networked observation tools (e.g., micro phasor measurement units (µPMUs)) and consequent cybersecurity threats through networks. This paper presents a secure, cost-effective, µPMUs-like metering method using a blockchain-assisted smart (BAS) inverters for a BTM solar system. The proposed BAS inverter consisting of an internet of things device as a node of a local blockchain network enables the secure provision of inverter measurement data for grid services. The BAS inverter sends the encrypted local measurement data with a timestamp to a local blockchain miner. Once the blockchain miner generates a tamper-resistant metering ledger including the measurements, it is used to assess the situational awareness of the BTM solar system. The concept of the proposed metering using the BAS inverters is validated by experimental studies.

A single-phase Cascaded H-Bridge (CHB) grid-tied multilevel inverter is introduced with a detailed discussion of the proposed novel neural controller for better efficiency and power quality in the integration of renewable sources. An LCL (inductor-capacitor-inductor) filter is used in the multilevel inverter system to achieve better harmonic attenuation. The proposed Neural Network (NN) controller performs the inner current control and tracks the references generated from the outer loop to satisfy the requirements of voltage or power control. Two multicarrier-based Pulse Width Modulation (PWM) techniques (phase-shifted modulation and level-shifted modulation) are adopted in the development of the simulation model to drive the multilevel inverter system for the evaluation of the neural control technique. Simulations are carried out to demonstrate the effectiveness and efficient outcomes of the proposed neural network controller for grid-tied multilevel inverters. The advantages of the proposed neural control include a faster response speed and fewer oscillations compared with the conventional Proportional Integral (PI) controller based vector control strategy. In particular, the neural network control technique provides better harmonics reduction ability.