In order to drive cost-effective investment in distribution grid while increasing shares of renewable energy, authorities and utilities are exploring ways to increase hosting capacities using connect-and-manage connection arrangements or non-wire alternatives with battery storages [1]. In the past, traditional connect-and-forget connections arrangements have been based on a worst-case scenario, with limited hosting capacities that would not be a problem without any operational action. However, recent changes in grid connection policy have led to a situation where distribution system operators (DSOs) are forced to actively take operational measures in the event of distribution constraints, even though this allows more renewable energy to be accommodated on the same line capacity [2,3].

Meanwhile, regulators around the world are considering ways to increase the visibility of distributed energy resources (DERs) and integrate them into the governance of wholesale markets by providing higher levels of economic compensation to DERs that contribute to the grid to improve system reliability [4,5,6]. This literature contains ideas for institutionalizing participation in wholesale electricity markets by aggregating many small DERs that physically connect the distribution grid through new market participants such as distributed energy resource aggregators (DERAs) or virtual power plants (VPPs). This research was motivated by the fact that the space in which DERAs are financially rewarded is the wholesale market at the transmission level, but the resources they manage are physically distributed widely across the distribution grid. If there is enough DERs connected to the grid that may cause distribution constraints, and they want to participate in the wholesale market through a DERA, the DSO needs to prequalify the DERA's bidding actions. The prequalification process allows DSOs to receive DERA's day-ahead and real-time market bids in advance, review them for potential grid constraint events, and implement a system where only bids that pass the process are forwarded to the wholesale market. In the absence of such a system, DERA may act only to maximize revenue in the wholesale market without considering distribution constraints, causing a situation where the reliability of the distribution grid is greatly threatened.

Previous studies have implemented and studied various forms of such schemes. In one study, the DSO calculates power flows based on the VPP's bids and responds by performing grid reconfiguration if it anticipates a constraint problem, and if there is still a problem, it calculates the sensitivity of each VPP's resources and the amount of violation and delivers it to the VPP. The VPP will then adjust their scheduling and submit it to the DSO. In this case, it is characterized by utilizing a periodic rolling horizon update method to respond to future uncertainties [7]. Another study proposed a scheme in which the DSO and TSO each operate an own balancing market, and DER bids go directly to the TSO market bids if they do not cause problems on the distribution grid, and if they do, they are cleared within the DSO's balancing market first and the adjusted bids go to the TSO market [8]. On the other hand, [9] and [10] proposed a system for DSOs to verify bids of DERAs based on robust optimization for handling uncertainty in generation and load. These two papers are unique in that they propose an interaction scheme designed based on the legal authority of each entity in the real world, and they present the idea of converting a robust optimization based on chance-constrained optimization into a cooperative solution based on linearized information exchange between entities. In the most recent work, the DSO calculates and communicates the expected distribution system constraints according to the VPP's bidding plan, and the VPP also estimates its own distribution system constraints based on multiple scenario forecasts and utilizes them in actual operations. In this paper, the process of the DSO communicating distribution system constraints to the VPP occurs only once and does not include an iteration process [11].

In addition to the above theoretical research, field experiments are also being conducted in various countries. The EU's SmartNet project sought to design an optimized interaction scheme between TSO-DSOs and proposed five representative TSO-DSO coordination schemes: Centralized AS market model, Local AS market model, Shared balancing responsibility model, Common TSO-DSO AS market model, and Inte-grated flexibility market model [12]. OMI Polo Español S.A., an electricity market operator in Iberia, conducted the IREMEL project to define the operating model for the regional electricity market and to design and demonstrate mechanisms to address distribution system constraints that arise from the participation of distributed resources in the wholesale market [13]. The COOIDINET project in Spain, Sweden, and Greece is a demonstration of DSOs and TSOs working together to implement a cooperative system in a reliable and efficient manner. The project designed a TSO-DSO cooperation system in which congestion management in the distribution system is first addressed at the local DSO level, with priority access to local flexibility resources, and only unused bids are forwarded to the higher level system operator [14]. These demonstration projects took into account the market rules and power industry structure in each jurisdiction where the demonstration was conducted.

In Korea, a demonstration study of the TSO-DSO-DERA coordination scheme was conducted from August 2019 to August 2023, leaded by KEPCO, considering the current Korean power industry structure and market rules. In particular, this project focused on the interaction between DSO and DERAs who want to participate in the TSO's market, and conducted a demonstration of prequalification scheme on two distribution lines of KEPCO. The reference [9] and [10] are the deliverables from this demonstration project. In addition to the day-ahead prequalification, this project also included a demonstration of the direct control scheme of the DSO in a real-time situation, which is beyond the scope of this paper and is not described. This paper aims to summarize and share practical algorithm improvements and lessons learned based on the algorithm in the previous study in [9], as well as validation through real-world demonstration sites.

In Section 2, roles and responsibilities of relevant actors in the proposed scheme are presented. Section 3 and 4 describe the overall scheme of the proposed prequalification process and details of internal prequalification algorithm of DSO, respectively. Section 5 provides results of field experiment of the prequalification scheme on the real demonstration sites in Korea. Section 6 presents the concluding remarks of this study.

In this section, we describe the roles and responsibilities of each actor involved in the DSO's prequalification scheme assumed in this study. This can be depicted as shown in Figure 1.

In order to understand the demonstration results of this paper, it is necessary to first understand the theoretical content of the prequalification scheme that the entire consortium research team was trying to implement, as well as the governance of the Korean electricity industry. The governance and actor-specific roles and responsibilities assumptions in this study are not based on exploring all the theoretically possible cases as in other previous studies, but rather on the premise of minimal effort to maintain as much as possible within the current structure and the recently revised laws and regulations. It can be said that the focus was on ensuring that there is not much difference between what was tested in the demonstration study and the system that will be implemented in the real world in the future.

In Korea, the DSO, the entity that actually implements and operates the prequalification scheme, is the state-owned power company, KEPCO. It directly operates transmission, distribution, and sales businesses and has six power generation companies as subsidiaries with a portfolio of large traditional power generation sources [15]. Retail business license raises a conflict of interest argument for KEPCO to act as a DSO, but the South Korean government attempts to control this through direct oversight. In practice, the resources of KEPCO's generation companies are mainly large coal, nuclear, and gas-fired generation, and they rarely enter the distribution-level DER market.

Meanwhile, South Korea recently enacted a special law to promote the installation of DERs, which will take effect in 2024. The law explicitly stipulates the operating rights and responsibilities of distribution business license holders. Currently, KEPCO is the sole distribution business license holder. The law allows DSOs to forcibly disconnect loads or DERs in emergency situations where reliability is threatened, but the right to curtail on a regular basis was excluded from the National Assembly's discussion. Therefore, individual arrangements for output control between DERs or DERAs representing them and DSOs are needed to resolve potential distribution constraints caused by DERs.

Because DERA is a private entity that recruits DERs to directly participate in the wholesale market and earn revenue, it is not responsible for the reliability of the distribution or transmission grid, but through the national grid code and the network access agreement with KEPCO, it can be disconnected due to the decision of a certain TSO or DSO if it threatens the reliability of the network. From DERA's perspective, it has an motivation to coordinate with DSOs to trade off the risk of total disconnection for the bounded risk of partial curtailment.

In South Korea, the Korea Power Exchange (KPX) operates the wholesale market and transmission system, similar to independent system operators in the United States. They are not directly responsible for coordinating and managing distribution system constraints caused by DERs, unless the constraints extend to transmission system issues, as the Electricity Business Act delegates operational responsibility for the power system below 154kV to KEPCO.

The DSO's prequalification module designed in the demonstration project interacts with DERA's bidding module as shown in the following figure. Before the DERA submits a bid to the wholesale market, the DERA requests a prequalification procedure for the bid to the DSO, and the DSO's prequalification module uses an internal algorithm to review the potential constraints of the distribution system based on the expected generation of the DER under the DERA's bid.

Here is a breakdown of the entire process shown in the figure above.

The DERA submits bids to the DSO prior to the participation of wholesale market between 00:00 and 09:00 on D-1. The DERA determines and submits initial energy and reserve bids based on the generation forecast of the DERs managed by itself and its bidding strategy. It is allowed to continuously modify the bids during these hours.

The proposed day-ahead prequalification algorithm is executed as a module of the DSO's internal distribution operation system. The algorithm is completed within 15 minutes between 09:00 and 09:15 on D-1. The algorithm performs robust optimization by considering the initial bids submitted by DERAs, the load of each distribution node predicted by the DSO itself, and the forecast error range of each node. The DSO performs the optimization with the goal of maximizing the sum of the DERAs' 24-hour generation in order to minimize the output curtailment of the DERAs. The result of the algorithm is the maximum biddable capacity of the DERs at each node and the upper and lower bounds of the biddable output for storage resources. The resulting bid-modification-guideline values are passed to the DERA. The DERA is obliged to comply with the modification guidelines for bids that are determined to threaten the reliability of the distribution system according to the grid access agreement with the DSO.

The DERA will modify the output setpoints of each resource in light of the bid-modification-guidelines received from the DSO and resubmit them to the DSO. The first modified bid submission will take place during D-1 09:15-09:30, and the prequalification algorithm for it will take place between 09:30-09:45. The second modified bid submission will take place between D-1 09:45 and 10:00, and the prequalification algorithm for it will take place between D-1 10:00 and 10:15. Since the second modified bid submission is the third bid submitted by the DERA, if it does not pass prequalification by this time, the DSO will be forced to apply the last provided bid-modification-guidelines to end the process. This is because, due to time constraints in real-world system operation, it is not possible to give an infinite number of chances, so in this demonstration study, we set a total of three chances including the initial bidding after trial and error. This can be customized according to the market operation timeline of each jurisdiction applying prequalification.

Qualified Bid will be delivered to the transmission-level wholesale market as the one of bids from conventional market participants.

In this section, we detail the formulation of the prequalification and the algorithm to solve it efficiently, based on robust optimization approach, so that the DSO can support the market participation of DERAs while maintaining the reliability of the distribution system. For this purpose, we use the following nomenclature.

Sets and Indices
$b,i, j, k$	Subscripts for buses
$l$	Subscripts for lines
$b_{rsk}, l_{rsk}$	Subscripts for risky buses and lines
$t, a$	Subscripts for DER and DERA
$s, s\text{'}$	Subscripts for iterations
$\mathcal{N},\mathcal{L}$	Set of buses and lines
${\mathcal{N}}_{rsk\_ovv}, {\mathcal{N}}_{rsk\_udv}, {\mathcal{L}}_{rsk\_uof}, {\mathcal{L}}_{rsk\_dof}$	Set of overvoltage, undervoltage risky buses and upper overflow, down overflow risky lines
${\mathcal{G}}_i^a\supset {\mathcal{S}}_i^a,{\mathcal{R}}_i^a$	Set of DER, storage, and DER with reserve of DERA a on bus i
$\mathcal{A}$	Set of DERA
${\mathcal{X}}_{PF}^s, {\mathcal{X}}_{V\_OV}^s, {\mathcal{X}}_{V\_UV}^s, {\mathcal{X}}_{F\_UF}^s, {\mathcal{X}}_{F\_DF}^s, {\mathcal{X}}_{bid}^s$	Set of result of power flow, worst overvoltage state, worst undervoltage state, worst upper overflow state, worst down overflow state and revised bidding in iteration s
Parameters
$G_{ij}, B_{ij}$	Conductance and susceptance of the line between bus i and j [p.u.]
$\overline{V}, \underset{\_}{V}$	Upper and lower limit of the voltage magnitude [p.u.]
${\overline{S}}_{f,l}$	Upper rate capacity of line l [p.u.]
${\overline{P}}_{g,t}$	Rate capacity of DER t [p.u.]
${\overline{∆P}}_{g,t},{\underset{\_}{∆P}}_{g,t}$	Increase and decrease controllable amount of DRES t [p.u.]
$p_{g,t}^{ini},q_{g,t}^{ini}$	Active and reactive power of DRES t in initial bidding of CVPP [p.u.]
$r_{u,t}^{ini},r_{d,t}^{ini}$	Ramp up and down reserve power of DRES t in initial bidding of CVPP [p.u.]
$\overline{p_{g,t}^{ini}},\underset{\_}{p_{g,t}^{ini}}$	Upper and lower active power of DRES t in initial bidding of CVPP [p.u.]
${\overline{P}}_{d,i},{\overline{Q}}_{d,i}$	Demand forecast of bus i [p.u.]
$\underset{\_}{\mathrm{p}\mathrm{f}}$	A lower limit of the power factor
${\sigma }_{g,t}^{high}, {\sigma }_{g,t}^{low}$	Upper and lower uncertainty range of output of DER t [p.u.]
${\sigma }_{d,t}^{high}, {\sigma }_{d,i}^{low}$	Upper and lower uncertainty range of demand forecast of bus i [p.u.]
${OV}_{i,a}^s, {UV}_{i,a}^s$	Overvoltage and Undervoltage violation on bus i of DERA a in iteration s [p.u.]
${UF}_{l,a}^s, {DF}_{l,a}^s$	Upper overflow and down overflow violation on line l of DERA a in iteration s [p.u.]
$S{n}_{ovp,i,j}^s,S{n}_{ovq,i,j}^s$	Overvoltage magnitude sensitivity of active and reactive power on bus j to bus i to in iteration s at overvoltage worst case
$S{n}_{uvp,i,j}^s,S{n}_{uvq,i,j}^s$	Undervoltage magnitude sensitivity of active and reactive power on bus j to bus i to in iteration s at undervoltage worst case
$S{n}_{usp,l,k}^s,S{n}_{usq,l,k}^s$	Squared complex flow sensitivity of active and reactive power on bus k to line l to in iteration s at upper overflow worst case
$S{n}_{dsp,l,k}^s,S{n}_{dsq,l,k}^s$	Squared complex flow sensitivity of active and reactive power on bus k to line l to in iteration s at down overflow worst case
${Allc}_{ov,i,a}^s,{Allc}_{uv,i,a}^s$	Allocation factor of overvoltage and undervoltage violation on bus i of DERA a in iteration s
${Allc}_{uf,l,a}^s,{Allc}_{df,l,a}^s$	Allocation factor of upper and down overflow violation on line l of DERA a in iteration s
${ϵ}_V, {ϵ}_F, {ϵ}_{Bid}$	Criteria of overvoltage security, overflow security and convergence of bid revision
Decision Variables
$p_{g,t}^s, q_{g,t}^s$	Active and reactive power of DER t in iteration s [p.u.]
$r_{u,t}^s, r_{d,t}^s$	Ramp up and ramp down reserve power of DER t in iteration s [p.u.]
$\overline{p_{g,t}^s},\underset{\_}{p_{g,t}^s}$	Upper and lower active power of DER t in iteration s [p.u.]
${∆p}_{g,t}^s, {∆q}_{g,t}^s$	Adjustment of active and reactive power of DER t in iteration s [p.u.]
$\overline{{∆p}_{g,t}^s}, \underset{\_}{{∆p}_{g,t}^s}$	Adjustment of Upper and lower active power of DER t in iteration s [p.u.]
$p_i^s, q_i^s$	Active and reactive injected power on bus i in iteration s [p.u.]
$\left|v_i^s\right|, {\theta }_i^s$	Magnitude and angle of voltage on bus i in iteration s [p.u.]
${\xi }_{g,i}^s, {\xi }_{d,i}^s$	Forecast error rate of the DER output and demand on bus i in iteration s
$s_{f,l}^s, p_{f,l}^s, q_{f,l}^s$	Complex, active and reactive power flow on line l in iteration s [p.u.]
${\theta }_{ij}^s,{\theta }_l^s$	Angle of voltage between bus i and bus j or on line l in iteration s [p.u.]
$u_{g,t}^s$	Dropout binary variable of DER t in iteration s
Auxiliary Variables
$p_{g,t}^{s,ab}, q_{g,t}^{s,ab}$	Absolute value of active and reactive power difference of DER t between initial bid and iteration s [p.u.]
$r_{u,t}^{s,ab}, r_{d,t}^{s,ab}$	Absolute value of Ramp up and ramp down reserve power difference of DER t between initial bid and iteration s [p.u.]
$p_{st,t}^{s,dch}, q_{st,t}^{s,dch}$	Active and reactive discharging power of storage DER t in iteration s [p.u.]
$p_{st,t}^{s,ch}, q_{st,t}^{s,ch}$	Active and reactive charging power of storage DER t in iteration s [p.u.]
$v_{st,t}^s$	Charging state binary variable of storage DER t in iteration s
Binary Variables
${\xi }_{g,i}^s, {\xi }_{d,i}^s$	Forecast error rate of the DER output and demand on bus i in iteration s