Electricity Pricing and Its Role in Modern Smart Energy System Design: A Review

From powering our homes to driving our economies, energy lies at the heart of humanity’s complex challenges in the modern era. This paper reviews the evolution of smart energy systems, examining their technological advancements and societal implications while proposing a future design framework emphasizing four key pillars: holistic resource optimization, adaptive intelligence, environmental harmony, and human-centered design. While they offer numerous benefits, such as enhanced energy efficiency and reduced carbon emissions, smart energy systems also face challenges. These include cybersecurity risks, the complexity of integrating diverse energy sources seamlessly, high upfront costs, and potential compatibility issues arising from evolving technologies. Overcoming these challenges will be crucial for unleashing the full potential of smart energy systems and facilitating their global adoption. Abundant opportunities for further research and development exist in this domain, awaiting exploration and advancement.

Section: Human-centered Designmentioning

confidence: 99%

The Future Design of Smart Energy Systems with Energy Flexumers: A Constructive Literature Review

Hu,

Bui

2024

“…Furthermore, SGs enable consumers to take on a novel role in the energy supply chain as "prosumers" [43]. Prosumers are people who not only utilize renewable energy but also produce some of it themselves.…”

Section: Introductionmentioning

confidence: 99%

The Research Agenda on Smart Grids: Foresights for Social Acceptance

Senyapar,

Bayindir

2023

The “smart grid” (SG) refers to an advanced electricity grid system that possesses the capability to effectively co-ordinate the activities of all its connected users, encompassing both energy producers and consumers, with the primary objective of ensuring the provision of a reliable, cost-effective, and environmentally sustainable electricity supply while also prioritizing security measures. Research on the adoption of SG technology holds significant academic value as it addresses the crucial issue of user resistance, which can impede or postpone the progress of SG initiatives. In this paper, bibliographic data obtained from Web of Science and Scopus documents on SGs are analyzed with two complementary methods of bibliometric and thematic analysis. The findings revealed that academic production in the field is in step with renewable energy use but tends to diminish. The authors, publications, and countries that shape the field have been identified. The in-depth thematic analysis uncovered that safety concerns, artificial intelligence, and electric vehicles are the main areas of study, and economic benefits are found to be more effective than environmental concerns in SG technology’s social adoption. Studies and policies on SGs should consider the shift in consumer and producer roles. By comprehensively examining the literature on the social aspects of SGs, which is an oft-neglected area, this study aimed to provide researchers and policymakers with fresh perspectives on how to expedite the adoption of this innovative technology, thereby facilitating the transition towards a sustainable, environmentally conscious, and fair energy system.

“…The study considers load variations during the day without considering the location of the cloud energy storage system in the power grid. For an overview of smart energy system optimization research considering the economics of integrating and controlling DGs and BESS, one can see [7]. As can be seen in [7] and in the literature review in this paragraph, optimization considering energy price is not about optimizing the size and locations of DGs and BESS in the system.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation and Energy Management of Units in Distribution Networks with Multiple Renewable Energy Sources and Battery Storage Based on Computational Intelligence

Barukčić,

Kurtović,

Benšić

et al. 2023

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively.