Demand response for real-time congestion management incorporating dynamic thermal overloading cost

https://doi.org/10.1016/j.segan.2017.03.002Get rights and content

Highlights

  • Determination of a dynamic thermal overloading cost.

  • Design of a demand response methodology for real-time congestion management.

  • Agent-based distributed intelligence to solve the congestions locally.

  • Local flexibility market to procure flexibility in a multi-actor setting.

Abstract

Capacity challenges are emerging in the low-voltage (LV) distribution networks due to the rapid proliferation of distributed energy resources (DERs) and increasing electrification of loads. The traditional approach of network reinforcement does not achieve the optimal solution due to the inherent uncertainties associated with the DERs. In this article, a methodology of real-time congestion management of MV/LV transformers is proposed. A detailed thermal model of the transformer is used in order to obtain the costs incurred by overloading. An agent-based scalable architecture is adopted to combine distributed with computational intelligence for the optimum procurement of flexibility. The efficiency of the proposed mechanism is investigated through network simulations for a representative Dutch LV network. Simulation results indicate that the methods can effectively alleviate network congestions, while maintaining the desired comfort levels of the prosumers.

Introduction

Driven by an effective international climate policy  [1], [2], the electrical distribution networks worldwide have been hosting an increasing share of renewable energy resource (RES) based distributed generation (DG) and new forms of load consumption such as electric vehicles (EVs), heat pumps (HPs), or electrical heating ventilation and air-conditioning (HVAC) systems. Along with a greener energy mix, these Distributed Energy Resources (DERs) bring forth operational challenges including voltage violations or thermal overloading of network assets  [3], [4]. Consequently, distribution system operators (DSOs) require to enhance monitoring and control ability in the network and a shift towards Active Distribution Networks (ADNs) becomes imminent  [5], [6], [7].

Congestions or thermal overloadings occur when the power flow through a network asset (e.g. lines, cables, transformers) exceeds its transfer capability. Although the network assets are generally designed to withstand loads beyond a certain margin, continuous overloading results in degradation of the insulation of the distribution cables and transformer windings  [8], [9]. The traditional approach of reinforcing the network assets in such cases, not only necessitates a huge investment, but will also be deemed redundant as the peak loads tend to appear only for few hours in a year  [3]. To circumvent the required investment, a number of direct and indirect control approaches have been studied to tackle congestion issues in the ADNs. While the direct approaches mitigate congestions by curtailment of load and local generation  [10], [11], or by influencing the voltage level at the secondary side of a smart MV/LV transformer  [12], [13], their indirect counterparts motivate individual prosumers with appropriate price and/or incentive-based Demand Response (DR) mechanisms  [14], [9]. The price-based methods include day-ahead dynamic tariff, distribution grid capacity market, intra-day shadow price, flexibility service market, etc. However, most of these mechanisms aim to influence the demand flexibility only, while neglect or partially address physical constraints of the distribution networks  [15], [16], [17]. Consequently, applications of the DR-based methods for managing real-time congestions are still quite limited.

Different types of multi-agent system (MAS) based DR mechanisms have been deployed to exploit the flexibility from the small-scale prosumers  [18], [19], [20], [21]. As highlighted in  [21], [22], [23], the agent-based PowerMatcher technology optimizes the potential for aggregated individual household devices to adjust real-time operation. At the same time, Universal Smart Energy Framework (USEF) has been recently introduced as a conceptual approach to manage congestions more efficiently  [24], [9], [25]. USEF combines the indirect and direct approaches of congestion management to enhance the flexibility in the distribution network and enables the DSO to obtain flexibility from a local capacity market to relieve network congestions. A more direct approach of graceful degradation complements the market-based control to curtail active power demand when adequate flexibility is not available in the market.

However, for a market-based DR mechanism, a sound methodology for real-time congestion management is significant in order to transform the realized ageing of the network assets to a corresponding monetary loss. Based on the recent developments, an integrated congestion management mechanism is proposed in  [9] for the residential distribution network incorporating dynamic thermal overloading model of a distribution transformer. However, in reality, procurement of flexibility in real-time appears to be a more complex problem involving scenarios with multiple market entities in the same congested network area  [26]. This work extends the market-based control proposed in  [9] further, incorporating computational intelligence in a multi-actor setting. The method will take advantages of the scalable architecture of the agent-based DR technology and detailed thermal model of oil-immersed transformers. A detailed case-study, involving 229 households is presented to illustrate the impacts and expected results of the proposed mechanism. The main scientific contributions of the paper are as follows:

  • Demand response methodology incorporating the overloading cost, calculated from the dynamic thermal model of the transformer.

  • Application of agent-based distributed intelligence to solve the congestions locally.

  • An appropriate local flexibility market to procure flexibility in a multi-actor setting.

The remainder of the paper is organized as follows: Section  2 presents the overview of the market-based control, Section  3 describes the thermal loading model of the transformer, the methodology of flexibility procurement is detailed in Section  4, while Section  5 provides the description of the test scenario and the assumptions adopted. Finally, simulation results are presented and analysed in Section  6, before summarizing and concluding with Section  7.

Section snippets

Flexibility in distribution network operations

Due to the increasing availability of the flexible domestic appliances and small-scale generation technologies like rooftop solar PV, market-based control of the distribution network has been drawing an extensive attention of late  [27], [28], [29]. Apart from introducing new market entities such as aggregators and energy service companies, this has principally paved the way for a more decentralized operation of the future power system.

Different types of flexibility arrangements have been

Thermal overloading of transformer

Overloading of the MV/LV transformer may occur due to increased loads at connection points, for instance–simultaneous charging of the large number of EVs or switching of domestic HPs. Although thermal overloading affects the insulation life-time of the transformer windings, thermal dynamics involved in the loading allows the transformer to be overloaded for a certain duration of time. Therefore, the procurement of flexibility needs to be aligned with actual status and corresponding cost of

Procurement of flexibility

Upon determining the overloading cost, the DSO requests flexibility from the aggregators in order to resolve the congestion. The task of flexibility procurement is carried out by the LFSA that works as the interface between the network agents and the aggregators. The estimated overloading cost is taken as the basis of the calculation and adequate flexibility is procured from the available offers of the aggregators.

Simulation setup

The proposed methodology is evaluated with a simulation case study in a residential Dutch LV network. The network consists of three feeders, namely Feeder A, Feeder B and Feeder C. Total number of households connected in the feeders are 95, 42 and 92 respectively. As shown in Fig. 4, a 500 kVA MV/LV transformer feeds the network from the MV bus. All of the feeders comprise of underground power cables. The network model is built in MATLAB environment using the open-source Power System Analysis

Numerical results

Snapshots of the simulation results of the case study are first presented for two consecutive winter days in the Netherlands. Subsequently, the annual performance of the proposed approach is analysed in terms of the cost savings and reduction in congestion duration.

Each of the three aggregators matches the local supply and demand of their contracted prosumers and calculates the internal equilibrium price, λ for the cluster. The per unit local price signal also reflects the loading status in

Conclusions

The focus of this work has been to propose a suitable mechanism for real-time congestion management in LV distribution networks. An agent-based system architecture has been adopted to solve the problems through distributed intelligence. The residential load profiles have been generated by a bottom-up approach involving dynamic control signals. A detailed thermal model of the transformer has been used to observe the impacts on the life time of the winding insulation in a LV network with a

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      Ref. [59] proposed a distribution locational marginal pricing strategy for effective utilisation of the flexibility offered by responsive consumers to alleviate grid congestion. A DR model for congestion management is presented in [60], in which the congestion cost is represented in terms of the ageing of distribution transformers due to overloading, and the Distribution System Operator (DSO) procures the flexibility of responsive consumers to shift their consumption based on the congestion cost, which, in turn, mitigates the transformer overloading. Electric springs-based grid-interactive DR schemes have also been explored extensively in the literature, especially to tackle reliability and stability concerns stemming from power fluctuations from intermittent renewable energy sources in modern power systems [69].

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