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“electricity Demand Response Programs: Balancing Supply And Consumption”

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Balancing Services: Definition, Background & Why We Need It

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By Daiva Stanelyte Daiva Stanelyte Scilit Preprints.org Google Scholar * , Neringa Radziukyniene Neringa Radziukyniene Scilit Preprints.org Google Scholar and Virginijus Radziukynas Virginijus Radziukynas Scilit Preprints.org Google Scholar

Accepted: 31 December 2021 / Revised: 31 January 2022 / Accepted: 4 February 2022 / Published: 23 February 2022

Pdf) Designing Demand Response Portfolios

(This article belongs to Selected Papers Special Edition of the 17th International Conference of Young Scientists on Issues in Energy and Natural Sciences (CYSENI 2021))

It is important for the power sector to analyze and determine the throughput of distribution capacity and apply new methods aimed at increasing the capacity of the transmission system. As a result, the switch to a modern grid is two-pronged, involving both technological and social modifications. Demand response (DR) redistributes consumption away from peak times when network load and costs are highest. This incentivizes customers to use electricity when supply is high and cheap due to various market mechanisms. Current DR policy proposals emphasize the importance of driving behavior change through competitive pricing and customer participation in reducing carbon emissions and implementing smart energy solutions (including monitoring tools, such as smart meters and applications). The Internet of things (IoT) has been implemented to ensure cost-effective and adequate adaptive monitoring of energy consumption and demand-side management (DSM). This article is based on the latest source research on DR implementation methods applied at the power distribution level. It describes the main concepts, classifications, and entities that implement the DSM program, and suggests new visions and prospects for DSM and DR. In addition, it discusses the potential application of blockchain technology to the energy internet.

It is estimated that by 2040 the market demand for electricity will increase by up to 30% compared to 2017. The increase is due to further urbanization, population growth and an increase in the number of devices used, all of which tend to put a strain on the existing power infrastructure. [1]. Grid overload results from the constant increase in peak power loads, inadequate real-time monitoring and the rapidly growing fault identification, automation, and integration of renewable energy sources (RES) [1, 2]. This adds to the operational complexity of the system, making it more difficult to maintain demand-generation balance and network stability [3]. On a connected grid, load variations will also have an impact on the distribution of electricity between surrounding districts [4]. Traditional generators must be prepared to offset the stochastics of renewable generators through additional services [5]. To maintain system stability, a strong control strategy must manage and protect the system from emergency or unexpected events, rapid load fluctuations, and short-term power outages [4, 6, 7, 8]. As the use of photovoltaic (PV) systems grows in power systems, it is very important for PV systems to offer network support tasks such as reactive power, transient voltage ride through, and frequency regulation, which are usually performed by conventional rotating machines [9] .

There is no universal solution for creating a sustainable energy system. The transition must be considered systematically, taking into account all possible technologies and methods. This can be achieved through demand management, conservation of electricity, investments in infrastructure, market mechanisms, and support services (i.e., phase-voltage balancing, load generation balancing by altering generation schedules to match demand, and reactive volt-amperes (VAR) support for voltage control), which is an important measure to increase the flexibility of the energy system.

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Renewable-based distributed energy resources (DERs) have grown rapidly due to incentive schemes and widespread involvement [10]. DER is a larger word that refers to technologies such as distributed generation (DG), backup generation, energy storage, and demand side management (DSM) [11].

DSM is a concept that provides tools for utilities to influence load profiles [3]. This allows customers to actively participate in the power grid and can greatly improve power system stability, efficiency, economy and dependability [12]. The reliability of a power system is increasingly dependent on its flexibility, which is increasingly difficult to achieve on the production side due to the widespread use of renewable energy sources [13]. DSM covers all demand-side initiatives aimed at reducing consumption/costs/emissions or increasing revenue from energy sales, including techniques to increase the energy efficiency of buildings [14]. DSM can achieve this flexibility with advances in smart grids by asking consumers to get involved in DR programs [13].

The US Department of Energy has the following formal definition of DR: changes in the customer’s end-user electricity load in the near future as a result of dynamic price and reliability data [5]. DR is part of the DSM which includes only non-permanent actions performed on request [14]. DR is a new solution based on flexible demand/load management for network balancing. To balance the grid and receive financial incentives, consumers can increase, decrease, or adjust energy consumption (for example, in industry—production lines, appliances, motors; and in the case of household consumers—washing machines, boilers, heaters, air conditioners, refrigerators, heat pumps, electric vehicles (EV), etc.) [15].

Based on the analysis of scientific articles and other literature, scientists and experts often use these terms interchangeably, making no clear distinction between them, as has been determined [1]. It’s important to consider the continent or country where the term is used, the applicable legal framework, and the specific characteristics of electricity. Thus, investigation is essential for this reason alone.

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Several stages of DSM and DR development have been identified. In France in 1956, the term DR was introduced to flatten the load curve. In the 1920s, Germany started using the term direct expense management. In Europe and the United States, the term DSM was introduced around 1974. Widespread use of the term DSM relates to changes in power grids, advances in electrification, development of industrial enterprises, increased loads, and failures of existing electrical systems. In about 1980, the DSM service classification was developed. The electricity market appeared approx. 1990. Worldwide, various DR programs were developed in 1990–2010, including air conditioning, heating, etc. Starting in 2010, intensive development focuses on projects with real-time markets, real-time trading, thermal energy storage, real-time balancing, and load balancing [16, 17]. End-user expenses are becoming more customizable through smart devices, which allow them to respond to changes in electricity prices or other special incentives from third parties [18]. As a result, the implementation of DR programs in power systems faces a major challenge in maintaining specific infrastructure for information and communication technology [19].

New technologies—intelligent and autonomous controllers, advanced data management software, and two-way communication between electricity suppliers and consumers—help develop automated and distributed SGs [20, 21]. Among these technologies are energy storage solutions, such as batteries, which meet peak demands or accumulate excess energy [22]. These smart technologies are integrated into various parts of the new generation of power systems, starting with production and ending with monitoring of power consumption, which aims to increase the efficiency and stability of the system [20]. Features for SGs such as DR and load control can prevent unnecessary installation of, for example, transformers, and extend their lifetime [23].

Aiming to expand and sustain the transition to low-carbon technologies and LGs, the institutional framework should be revised for electricity generation, distribution and consumption models. However, the available research is not sufficient to explain the institutional dynamics especially the socio-technical changes which should be considered to better understand the development and implementation of DR [22]. Evidently, there is a need to further explore the role of energy companies in supporting energy efficiency and savings among end users, given the complex interrelationships between different policy objectives, energy company operations, and perspectives of energy end users [24]. In addition, insufficient focus is placed on intermediaries called aggregators, which are very important in the development of DR [22].

This research is an important step in assessing interest in the DSM program and electricity tariffs. This paper closes a gap in understanding the aggregator and its role in the market.

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The methods presented in the research are general in nature and can be applied in any country

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