A Flexibility Market Platform for Electricity System Operators Using Blockchain Technology

The paper’s main objective is to demonstrate the trading and flexibility of services amongst TSOs, DSOs, and Prosumers in a transparent, secure, and cost-effective manner using Blockchain-based TSO-DSO flexibility marketplace (EFLEX). The aim is to look for ways to help DSOs/TSOs be more flexible and more directly engaged in managing energy flows on the network. EFLEX will streamline the needs of both TSO and DSO on the same platform. Based on the paper’s proposed services, the pilot service demonstration will be carried out in Bulgaria and Romania, and the main focus will be on congestion management, TSO-DSO Coordination, and Marketplace. The proposed objective is achieved by using Blockchain-based smart contracts and distributed ledger technology.

Optimising Energy Management in Hybrid Microgrids

This article deals with the optimization of the operation of hybrid microgrids. Both the problem of controlling the management of load sharing between the different generators and energy storage and possible solutions for the integration of the microgrid into the electricity market will be discussed. Solar and wind energy as well as hybrid storage with hydrogen, as renewable sources, will be considered, which allows management of the energy balance on different time scales. The Machine Learning method of Decision Trees, combined with ensemble methods, will also be introduced to study the optimization of microgrids. The conclusions obtained indicate that the development of suitable controllers can facilitate a competitive participation of renewable energies and the integration of microgrids in the electricity system.

An Overview of System Strength Challenges in Australia’s National Electricity Market Grid

The national electricity market (NEM) of Australia is reforming via the rapid uptake of variable renewable energy (VRE) integration concurrent with the retirement of conventional synchronous generation. System strength has emerged as a prominent challenge and constraint to power system stability and ongoing grid connection of VRE such as solar and wind. In order to facilitate decarbonization pathways, Australia is the first country to evolve system strength and inertia frameworks and assessment methods to accommodate energy transition barriers, and other parts of the world are now beginning to follow the same approach. With the evolvement of the system strength framework as a new trending strategy to break the transition barriers raised by renewable energy project development and grid connection studies, this paper provides a high-level overview of system strength, covering such fundamental principles as its definition, attributes, and manifestations, as well as industry commentary, cutting-edge technologies and works currently underway for the delivery of a secure and reliable electricity system with the rapid integration of inverter-based resources (IBRs) in the NEM grid. The intent of this study is to provide a comprehensive reference on the engineering practices of the system strength challenge along with complementary technical, regulatory, and industry perspectives.

Modelling of the Ability of a Mixed Renewable Generation Electricity System with Storage to Meet Consumer Demand

In this paper, we explore how effectively renewable generation can be used to meet a country’s electricity demands. We consider a range of different generation mixes and capacities, as well as the use of energy storage. First, we introduce a new open-source model that uses hourly wind speed and solar irradiance data to estimate the output of a renewable electricity generator at a specific location. Then, we construct a case study of the Great Britain (GB) electricity system as an example using historic hourly demand and weather data. Three specific sources of renewable generation are considered: offshore wind, onshore wind, and solar PV. Li-ion batteries are considered as the form of electricity storage. We demonstrate that the ability of a renewables-based electricity system to meet expected demand profiles can be increased by optimising the ratio of onshore wind, offshore wind and solar PV. Additionally, we show how including Li-ion battery storage can reduce overall generation needs, therefore lowering system costs. For the GB system, we explore how the residual load that would need to be met with other forms of flexibility, such as dispatchable generation sources or demand-side response, varies for different ratios of renewable generation and storage.

Membership Function Optimization of Fuzzy Inference System Using Cuckoo Search Algorithm for Peak Load Forecasting in National Holiday

This study aims to analysis peak load prediction of Indonesian national holidays for Jawa-Bali electricity system. Forecasting applied using the Fuzzy Logic System (FLS) method combined with the Cuckoo Search Algorithm (CSA). CSA is used to determine the optimal membership function in fuzzy logic. Cuckoo search algorithm has a very good performance in terms of optimization. This method is applied for short-term load estimates on holidays/special days on the Jawa-Bali electricity system, Indonesia. The study used data from daily peak loads during Indonesian national holidays in 2014 on the Jawa-Bali electricity system. The data analyzed is the daily peak load documentation data for 4 days before national holidays and during national holidays in 2014. Testing the simulation results, it was found that the Fuzzy Logic System - Cuckoo Search Algorithm (FLS-CSA) method gives good forecasting results, this is evidenced by using the mean absolute percentage error (MAPE). Forecasting results using the Cuckoo Search Algorithm (CSA) optimization method on fuzzy logic membership functions for peak loads on national holidays on the Java-Bali 500kV electrical system give satisfactory results with an average forecasting error of 1.511314562%.

Modelling the Transition towards a Carbon-Neutral Electricity System—Investment Decisions and Heterogeneity

To achieve the climate goals of the Paris Agreement, greenhouse gas emissions from the electricity sector must be substantially reduced. We develop an agent-based model of the electricity system with heterogeneous agents who invest in power generating capacity under uncertainty. The heterogeneity is characterised by the hurdle rates the agents employ (to manage risk) and by their expectations of the future carbon prices. We analyse the impact of the heterogeneity on the transition to a low carbon electricity system. Results show that under an increasing CO2 tax scenario, the agents start investing heavily in wind, followed by nuclear and to some extent in natural gas fired power plants both with and without carbon capture and storage as well as biogas fired power plants. However, the degree to which different technologies are used depend strongly on the carbon tax expectations and the hurdle rate employed by the agents. Comparing to the case with homogeneous agents, the introduction of heterogeneity among the agents leads to a faster CO2 reduction. We also estimate the so called “cannibalisation effect” for wind and find that the absolute value of wind does not drop in response to higher deployment levels, but the relative value does decline.