Pertti Jarventausta

Clean transition increases the demand for reliable electricity distribution, but while the capacity can be improved through investments, responding to the demand increases costs for the customers. This study presents a methodological improvement to the assessment of the reasonability of pricing, by comprehensively analyzing pricing regulation data to define the total cost of electricity distribution by clustering. A novel systematic view on the volume and distribution of economic steering shows that according to the regulation data in Finland, the total annual cost of distribution for the present level of reliability varies from EUR 490/a in an urban environment to EUR 1220/a per customer in sparsely populated areas. The majority of the total costs of distribution stem from actual utility expenses. The approach and results may be used for implementing TOTEX models for future pricing regulation.

The cost reflectivity of electricity distribution network tariffs has been debated in several countries, and various ways to enhance it have been investigated in recent years. However, the recent academic literature regarding the approach based on cost causation has a clear gap because no case studies show how distribution network tariffs can be determined in practice for large customer groups. This article offers a calculation methodology to determine distribution network tariffs based on cost causation along with a case study where unit prices are determined for the tariff structures still widely used today using the data for two separate network areas being operated by two Finnish distribution system operators (DSOs) in an unbundled electricity market environment. The results of the case study show that the total differences between the target and the realized turnovers in both investigated networks are smaller than 1%, which means almost a full cost recovery. In addition to traditional tariff structures, the proposed calculation methodology can also be modified to design and determine other pricing schemes. The need for systematic calculation processes is growing to improve the cost reflectivity of present tariffs and adapt to the needs of the evolving operating environment, novel tariff structures, and new emerging customer groups.

In support of the global green transition, numerous policies have been introduced to efficiently address the increasing demand for reliable electricity. However, the impacts of these policies have received limited attention, despite the potential for unsuccessful policy targets to introduce inefficiencies into the energy system, subsequently diminishing societal wealth. This study bridges this research gap by conducting a comprehensive examination of a supply reliability incentive within electricity pricing regulation, aiming to contribute new insights for policy assessments. Analyzing data from all electricity distribution operators within a single jurisdiction, the study investigates the volume and distribution of economic steering to elucidate the overall societal impact. The findings suggest a rewarding system for positive developments in indices, regardless of the absolute interruption index levels, highlighting the importance of precise variable definitions in implementing incentive mechanisms. The assessment tools developed for this study will be valuable for further regulation and policy assessments.

Distributed electric power production by small-scale customers is increasing continuously. Photovoltaic production is a popular method of producing self-energy for customers. Additionally, power systems require more flexibility when weather-dependent renewable energy production increases. Small-scale customers can increase the self-consumption of self-produced energy by using batteries or a demand response operation. However, batteries require high investment, and demand response operations induce a loss of comfort. Customers who heat their buildings using electric heaters are a good target for demand response operations because their heating can be controlled with limited changes in the indoor temperature. The demand response potential of a building can be defined by simply using customer load profiles and knowledge of the outdoor temperature. Any other information is not required in the proposed novel method. A tolerable variation in indoor temperature corresponds to considerably smaller battery capacity, though it is still a significant amount. With an optimally sized photovoltaic system, it is possible to use both methods simultaneously to increase self-consumption. Maximal benefits can be attained from both methods if the battery system is used as a primary control and the demand response is used as a secondary control. The defined novel method for determining the demand response potential of small-scale customers can also be used when estimating the flexibility of a large customer group. Small-scale customers together can provide significant flexible capacity when their electrical heating is centrally controlled.

The sub-aggregation of electric vehicles provides significant potential to power systems in the form of ancillary services. This means with smart charging it is possible to shift loads from peak to off-peak hours. For the flexibility from privately owned electric vehicles to be offered to the electricity market, customer participation is crucial; however, the impacts of sub-aggregation on customers have not been studied thoroughly. In this paper, charging data covering over 80,000 real-world charging sessions from various commercial charging sites are introduced and the charging characteristics are analyzed. Importantly, a method for finding an optimal pattern for load reduction with the least impact on customers is presented. We suggest an optimal schedule for load reduction from the customer viewpoint at different types of charging sites, including public car parks, offices, residential sites, and shopping centers. The findings indicate that residential and office charging sites offer the greatest potential for load reduction with the least impact on customers. The most flexibility is available during peak charging hours, which on average are at 08:00 at car parks, 07:30 at office sites, 19:00 at residential sites, and 10:00 at shopping centers.

The share of electric vehicles (EVs) in the market has been growing rapidly during the past few years. Much research has been conducted in order to enable smart charging, which would fulfil EV user needs while considering technical limitations of the grid and incentives provided by the electricity market. However, the major part of the developed charging methods uses information of EV near-future driving profiles, e.g. departure time and energy need of the next trip, which can be problematic to achieve accurately enough. This kind of information will require either an extra communication link between the EV user and the EV charging controller, or EV tracking. Regardless of how the information is acquired, some uncertainty is likely in the predictions, which can cause undesired results. The aim of this paper is to assess these prediction error-related issues for distributing available charging capacity and to introduce alternative EV charging control methods that eliminate prediction error-related challenges. The results indicate that the EV battery state-based methods can distribute available charging capacity almost as effectively as the EV near future driving profile-based method with perfect predictions. Copyright © 2020 Praise Worthy Prize - All rights reserved.

The profitability of domestic battery energy storage systems has been poor and this is the main barrier to their general use. It is possible to increase profitability by using multiple control targets. Market price-based electricity contracts and power-based distribution tariffs alongside storage of surplus photovoltaic energy make it possible to have multiple control targets in domestic use. The battery control system needs accurate load forecasting so that its capacity can be utilized in an optimally economical way. This study shows how the accuracy of short-term load forecasting affects cost savings by using batteries. The study was conducted by simulating actual customers’ load profiles with batteries utilized for different control targets. The results of the study show that knowledge of customers’ load profiles (i.e., when high and low peaks happen) is more important that actual forecast accuracy, as measured by error criteria. In many cases, the load forecast based on customers’ historical load data and the outdoor temperature is sufficient to be used in the control system, but in some cases a more accurate forecast can give better cost savings.

This paper discusses the concept of using AMR (automatic meter reading) connected distributed loads as fast frequency reserves (ACFRs). The paper presents high-level analysis on power system response when considerable amount of directly grid-connected rotational reserves are replaced by AMR meter connected loads. In the study, the amount of ACFRs and their response time (i.e. very fast and fast) are varied together with different rotational kinetic reserves of power generation participating in frequency control. For the studies, a general power system and load models for EMT-type software have been established. The results show that ACFR could in principle replace effectively rotating reserves provided that frequency measurement and the coordination of the ACFRs are robust. The studied load shedding patterns and parametrization have less than excepted influence on frequency minimum and system response.