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To operate with high efficiency and minimise the risks of power failures, power systems require careful monitoring. The availability of real-time data is crucial for assessing the performance of the grid and assisting operators in gauging the present security of the grid. Traditional supervisory control and data acquisition (SCADA)-based systems actually employed provides steady-state measurement values which are the calculation premise of State Estimation. More often, however, the power grid operates under dynamic state and SCADA measurements can lead to erroneous and inaccurate calculation results. The introduction of the phasor measurement unit (PMU) which provides real-time synchronised voltage and current phasors with very high accuracy is universally recognised as an important aspect of delivering a secure and sustainable power system. PMUs are a relatively new technology and because of their high procurement and installation costs, it is imperative to develop appropriate methodologies to determine the minimum number of PMUs as well as their strategic placements to guarantee full observability of a power system. Thus, the problem of the optimal PMU placement (OPP) is formulated as an optimisation problem subject to various constraints to minimise the number of PMUs while ensuring complete observability of the grid. In this chapter, integer linear programming (ILP), genetic algorithm (GA) and non-linear programming (NLP) constrained models of the OPP problem are presented. A new methodology is proposed to incorporate several constraints using the NLP. The optimisation methods have been written in Matlab software and verified on the standard Institute of Electrical and Electronics Engineers (IEEE) 14-bus test system to authenticate their effectiveness. This chapter targets United Nations Sustainable Development Goal 7.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

Energy production and distribution is undergoing a revolutionary transition with the advent of disruptive technologies such as the Internet of Energy (IoE), 5G and artificial intelligence (AI). IoE essentially involves automating and enhancing the energy infrastructure: the power grid from grid operators to energy generators and distribution utilities. The IoE also relies on powerful connectivity networks such as 5G, big data analytics and AI to optimise its operation. By incorporating the technology that employs ubiquitous devices such as smartphones, tablets or smart electric vehicles, it will be possible to fully exploit the potential of IoE using 5G networks. 5G networks will provide high speed connections between devices such as drones, tractors and cloud networks, to transfer huge amounts of sensor data. Additionally, there are many sources of isolated data across the main energy production units (generation, transmission and distribution), and the data is increasing at phenomenal rates. By applying AI to these data, major improvements can be brought at each stage of the energy production chain. Tying renewable energy to the telecommunications sector and leveraging on the potential of data analytics is something which is gaining major attention among researchers and industry experts. This chapter therefore explores the combination of three of the most promising technologies i.e. IoE, 5G and AI for achieving affordable and clean energy, which is SDG 7 in the UN Sustainable Development Goals (SDGs).

This chapter examines the potential of solar energy for the development of sustainable tourism in Croatia. Tourism is an important economic activity in the Croatian economy due to the mild climate and many sunny days. Solar energy photovoltaic and thermal systems can help to support sustainable tourism, as well as increase employment and cooperation between local and national governments. This study compares best practices in solar energy for the Mediterranean countries of Italy, Spain, Cyprus, and Greece. The Mediterranean Basin is a strategic development area for the European Union, and solar energy will help to maintain its stability and high-quality standards of living.

The turbulent phase of COVID-19 has caused uncertainty as governments fail to develop coherent strategies for cutting emissions and are struggling to match the rhetoric of sustainable activities with actions (Barbier & Burgess, 2020; Cawthorn, Kennaugh, & Ferreira, 2021). In the recent past, firms have failed in their plans to decarbonise their key sectors such as the retail sector in the United Kingdom So far, retailers' commitment to achieving net zero emissions has been an important pledge but delivery is nowhere closer to their promises (Henriques, 2020). The firms' climate targets are not going to be met by magic as serious action is needed to fulfil the promises.

Fossil fuels have led to a drastic increase in carbon emissions in the world over the last decade. Firms championing cleaner energy and low carbon technologies are needed to cut emissions. Renewable energy sources such as wind energy can help reducing the dependency of fossil fuels (Boretti, 2020; Ebhota & Jen, 2020). Wind is an indirect form of solar energy which can provide environment-friendly option in uncertain times and can provide long-term sustainability of global economy. Solar energy technologies have the potential to decrease climate change through energy-related emissions (Li, Dai, & Cui, 2020). Increasing energy demand has initiated a focus on using hydrogen from water as a substitute for oil and fossil fuels (Boretti, 2020).

The first part of the chapter discusses theoretical perspectives of sustainable development and environmental performance with regards to three main issues: energy, water and carbon emissions, whereas the later part highlights the importance of solar technology as a low-polluted alternative to fossil fuels in the retail sector. Sustainable development of energy, water and environmental precautions such as reducing carbon emissions are of interest to wider branches of industries including retail, energy and water sector, governmental policymakers, researchers, educators and society. The purpose of this chapter is to increase the debate of the key issues of sustainable development regarding environment, energy and water in the modern times.