Ancillary services provision by aggregators and impact on distribution network operation

The role of smart communities integrated with renewable energy resources, smart homes and electric vehicles in providing ancillary services: A tri-stage optimization mechanism

Many electricity markets now co-optimize production and pricing of ancillary services such as contingency reserve and regulation with that of energy. This approach has proved successful in reducing ancillary service costs and in providing consistent pricing incentives for potential providers of both energy and ancillary services Here we discuss the basic concepts involved, the optimization formulations employed to clear such co-optimized markets, and some of the practical issues that arise.

Clean energy resources, like wind, have a stochastic nature, which involves uncertainties in the power system. Introducing energy storage systems (ESS) to the network can compensate for the uncertainty in wind plant output and allow the plant to participate in ancillary service markets. Advance in compressed air energy storage system (CAES) technologies and their fast response make them suitable for ancillary services. This paper investigates the participation of a combined energy system composed of wind plants and compressed air energy storage system (CAES) in the energy market from a private owner’s viewpoint, including trading in energy markets and bidding for frequency regulation and reserve capacity in ancillary service markets. Since this problem contains various uncertainties associated with market prices, wind generation levels, and regulation signals, distributionally robust optimization (DRO) is used to model the uncertainties and enhance the simultaneous participation of a combined wind-CAES system in day-ahead energy and ancillary service markets. This method combines the advantages of stochastic and robust optimization. In contrast to robust optimization (RO), the method consolidates specific statistical data to reduce conservative results. Simulation results demonstrate the proposed model’s effectiveness in handling uncertainties and provide a framework for investors in this area. In addition, case study analyses are applied to assess the model’s performance and validate the coordination of a wind plant and compressed air energy storage system in participating in a deregulated electricity market. Finally, DRO and RO are compared in modeling the uncertainties of the optimization problem. The optimal outputs demonstrate the effectiveness of DRO in terms of achieving higher realized profits with less conservative results.

Recent advancements in battery technology have made them more economically viable than ever before, making them suitable for various grid-scale applications. Due to their rapid response, batteries are attractive for providing ancillary services (AS), such as frequency regulation and reserve services, to the bulk power grid. On the other hand, an electric vehicle (EV) is viewed as a moving battery; consequently, EVs are also suitable for those services. This research proposes a linear planning model for an Electric Vehicle Aggregator (EVA) within a distribution network (DN) to offer Ancillary Services (AS) to the bulk power grid. The model takes into account contract design by identifying optimal incentives or charging tariffs that Electric Vehicle (EV) owners would be willing to pay the EVA for charging their vehicles. Additionally, the model takes into account the size of the electric vehicle (EV) fleet as a crucial planning factor for EV aggregation that depends on the energy pricing set by the EVA. The proposed model is developed to maximize the overall profit of bidding capacities in the energy and AS markets while supporting the operation of an unbalanced DN by maintaining the DN limits. Simulation results and sensitivity analyses on the model have been carried out to support the investment model and investigate the change in the optimal solution across different case studies. Simulations show that the optimal charging tariff ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> ) is ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.02~{\$}/kWh$ </tex-math></inline-formula> )when considering scenarios where Distribution Network (DN) limits, such as thermal and voltage constraints, are ignored for all EV participation versus <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> relations. When including the DN, the optimal payoffs vary based on the relationship between the charging tariff and the number of participating EVs.

Smart grids have benefits such as high reliability and availability due to distributed energy resources (DERs) which are integrated to the power transmission and distribution grid. These DERs will maintain an uninterrupted power supply for electricity consumers in a case of isolation from the primary electricity grid due to failure of the system. The integration of DERs into the transmission and distribution grid introduces new consequence for distributed network operators (DNOs). In traditional electricity grid, power flow is happening normally from the bulk generation stations to consumers and all control operations are done at the centralized control station. In the modern electricity grid with DERs, the energy follows in both directions via substations due to randomly spread energy sources at the lower voltage side. Although distribution network was considered as a static system without major control operations or reconfiguration requirements. For a smart grid, distribution network can be considered as a dynamic system due to DERs. Power routing is an emerging concept. Its main purpose is to make the distribution network more flexible which will help to solve the technical issues related to DERs. With the introduction of DERs, DNOs have faced many challenges. Communication is one of major challenges in DNO Technology. Nowadays most of the communication architectures are based on the Internet that creates major security issues with the system. Cyber-security Enabled Communication Architecture is the most important concept for Power Routing. In this paper we introduce a cyber-security enabled communication architecture that facilitates the security for the information of an electricity grid.

The recent developments in electric vehicles (EVs) causes several issues that have not been satisfactorily addressed. One of the foremost problems is the charging–discharging processes of EV batteries with diverse characteristics. Although a charging station is the first choice in this regard, a battery swap station (BSS) is also a suitable alternative solution as it eliminates long waiting periods and battery degradation due to fast charging. BSS has the capability to ensure prompt and efficient service for electric vehicles. Since BSS has a large number of battery systems, optimum planning of the charging–discharging operations of the batteries is critical for both BSS and the grid. This study presents an optimal charging–discharging schedule for multiple BSSs based on the swap demand of privately owned EVs and electric bus (EB) public transportation system. In addition, BSSs reinforce the power grid by providing ancillary services such as peak shaving and valley filling with demand response programs. In order to increase the flexibility of the operation, the mobile swapping station (MSS) concept, an innovative and dynamic service, is introduced to the literature and added to the model. The results indicate that BSS is an essential agent in the ancillary services market and the MSS concept is a yielding solution for both BSSs and power networks. Last, the data utilized in the study for swap demand calculation and power grid analysis are real-world data from Berlin, Germany.

In an effort to improve the stability and secure operation of the grid, regulatory bodies are opening Ancillary Services Markets participation to Distributed Energy Resources (DERs), energy storage systems, and demand response. Within this framework, this study proposes a model that simulates the coordinated operation of an aggregate of power plants, including non-dispatchable DERs and, as regulating units, Combined Heat and Power (CHP) generation and electrochemical energy storage systems. A Monte Carlo procedure is adopted to realistically create a population of aggregation scenarios. The real-time operation of the DER portfolio is managed through a Heuristic Greedy-Indexing logic, which allows the Aggregator to select the optimal control action to implement according to the technical and economic quantities characterizing the market and the grid. The techno-economic performance of the proposed algorithm is evaluated by simulating its interaction with the electricity markets. Finally, a sensitivity analysis is performed to analyze the profitability in different scenarios. The novel mathematical model proposed showed to be effective in managing a complex problem like the one at hand with an acceptable computational effort. The numerical results obtained confirmed that the aggregated participation in the market could provide interesting economic returns, especially if a CHP unit is involved as regulating unit, while the feasibility of the batteries adoption is still limited by the actual cost of the technology.

A regulatory effort is expected to be undertaken in the next years in order to foster the transition towards “smart grids”. In this view, the ancillary services market framework needs to change in order to cater for the possible contribution coming from distribution networks. Recently, the Italian Authority for Electricity, Gas and Water proposed three new market models aiming at exploiting the contribution of the resources connected to distribution networks for the ancillary services provision to the bulk grid. In this context, storage systems could enhance significantly the availability of ancillary services meanwhile allowing a better management of distribution networks. This paper aims at investigating the possible contribution of storage units in the ancillary services provision by distribution networks.

In evolving distribution networks, Distribution Network Operators (DNOs) are turning to Distributed Energy Resource (DER) to acquire network flexibility services to actively manage network needs, reducing and/or deferring the need for traditional reinforcement. NIE Networks have undertaken the FLEX project which seeks to trial a network flexibility market in Northern Ireland. This paper reports the high-level methods employed by GB DNOs to procure and use flexibility, also drawing on relevant learnings from other sources such as flexibility procurement platforms. Using the information compiled, this paper presents pertinent decision points which will feed into a DNOs&apos; business plan to procure flexibility services.

The present paper deeply analyzes the main features and the preliminary results of the UVAM project: a pilot project launched in 2017 by the Italian Regulatory Authority that aims to investigate the technical and economic feasibility of the provision of ancillary services by Distributed Energy Resources (DERs), controllable loads, energy storage systems and e-mobility. Indeed, the rising of renewables and DERs penetration in the power system recently called for the enlargement of the pool of actors that can actively participate in the grid balancing. The UVAM project represents the first opening of the Italian Ancillary Service Market to distributed resources and, for the first time, a capacity remuneration is also added to remunerate the provision of ancillary services. The results for the period November 2018 - March 2020 are analyzed both from technical and economic perspective, highlighting the main difficulties faced in the experimentation, while trying to interpret the behavior adopted by Aggregators in this first phase of the project.

As electric vehicles (EVs) uptake in the distribution networks continue to increase, it concerns distribution network operators (DNOs) that higher demand due to the charging of the EVs could undermine power quality (PQ) of their network. Of interest are challenges relating to undervoltage and thus the increase in electrical power losses. These challenges are exacerbated if EVs are charged uncoordinated. Therefore, in this paper, an optimized approach is proposed for the charging and discharging of EVs in a typical UK low-voltage distribution network with the goal to provide ancillary services to the DNOs. This is particularly in the context of voltage profile improvement and consequently electrical power loss minimization. The problem was formulated as a quadratic optimization task and solved using MATLAB. Results demonstrate that while uncoordinated charging could pose a challenge to the system operation, smart charge and discharge strategy of EVs can indeed offer power quality ancillary services (voltage profile improvement) to the system operator.

Distribution networks' energy losses versus hosting capacity of wind power in the presence of demand flexibility

The exploitation of Demand Response as an effective dispatching resource is strictly linked to the new economic opportunities for consumption units deriving from the participation to the Ancillary Services Market. This study provides evidence of the fact that Data Centers, thanks to backup generation and storage systems present at their sites, can be exploited as flexible consumption units for Demand Response purposes. Results highlight how the remuneration mechanisms foreseen for the market activity, and the sizing and operation of local assets, are of utmost importance in guaranteeing the worth of potential investments made by Data Centers' owners to adapt the consumption sites for the provision of ancillary services.

This paper proposes a hierarchical multi-stage approach based on a distributed level phasor measurement unit (µPMU) at local controllers and a phasor data concentrator (PDC) at the central control unit to restore system voltage when it exceeds the limits recommended by the IEEE 1547-2018 standard. The proposed algorithm does not need an accurate system model or employ optimization solutions. Therefore, it has less implementation complexity and would be popular among distribution network service providers (DNSPs) and distribution network operators (DNOs) as it does not suffer from cost and computational complexity limitations. A PMU-PDC-based communication platform has been developed as a more efficient alternative to the supervisory control and data acquisition (SCADA) system, and provides superior characteristics, including a higher sample rate, higher data resolutions, and faster communication. The proposed coordinated algorithm aims to postpone power generation curtailment in distributed energy resources (DERs) with overvoltage problems (local DERs) by incorporating all the DERs that are not subjected to voltage violation (remote DERs) by absorbing their maximum reactive power capacity. If the system voltage has not recovered after absorbing all of the reactive power capacity of all the DERs, a reduced active power curtailment proposed by the algorithm is then applied to the system to control the voltage. The proposed strategy has been simulated in MATLAB and applied to IEEE 13-bus and IEEE 33-bus radial distribution benchmark systems to validate the performance of the system, in terms of its ability to coordinate voltage control and the accuracy of the PMU-PDC-based communication interface.

Multi-stage optimization for energy management and trading for smart homes considering operational constraints of a distribution network