Distribution Market Operator Research Articles

Distribution Market Including Prosumers: An Equilibrium Analysis

We consider a distribution system including a supplying utility, prosumers, and conventional consumers, interacting through a distribution market. In such a market, each prosumer, seeking its maximum profit, uses an optimization problem to determine its hourly production/consumption, and the distribution market operator uses a minimum social-cost problem from which hourly locational marginal prices (LMPs) are derived. Conventional consumers accept these prices and the supplying utility sell energy at given prices at the substation bus. Since these problems (those of the prosumers and the one of the operator) are interdependent, they need to be jointly considered and solved, which constitutes an equilibrium problem. We show that such equilibrium problem is equivalent to an optimization one, solve it, and analyze outcomes using a realistic distribution system. We extract conclusions of the potential impact of this distribution market on the financial outcomes of the utility, the prosumers and the conventional consumers.

Optimal market-based operation of microgrid with the integration of wind turbines, energy storage system and demand response resources

This paper deals with an optimal operation of a microgrid in the electricity market and presents the communication between the distribution market operator and microgrid operator. The distribution market operator controls and manages the electricity market established in the distribution level, determining the amount of both electricity price and power exchange between market participants. The microgrid operator is able to purchase active and reactive power from the local distribution market. An effective short-term scheduling of the microgrid is implemented to ensure optimal operation. A risk-based stochastic model is used to model the prevailing uncertainties such as loads, wind generation, and main-grid availability in a market-based operation framework. Moreover, in this model, a linearized AC power flow is added to the mathematical formulations to offer a comprehensive solution to the security-constraint operation of the microgrid. The stochastic operation strategy is formulated as a mixed integer linear programming problem. Regarding the uncertainty modeling, the substation equipment failure is modeled with Monte-Carlo algorithm. The effectiveness of the risk-based stochastic method is demonstrated using a microgrid test bed in the presence of demand response resources, dispatchable and wind generation units as well as energy storage system. The results demonstrate demand response program can significantly reduce the operation cost in worst scenarios. Also, it is indicated that the risk-averse decisions reduce the risk of experiencing costly scenario. The proposed framework incorporating the distribution market constraints reduces the uncertainty in real-time operation as it can specified the required energy before running the problem. The deviations from assigned energy to the microgrid are penalized through the distribution market operator.

Day-ahead bidding strategies of a distribution market operator in a coupled local and central market

Smart energy systems lead to integrating more distributed energy resources and this creates the need for introducing a local electricity market within the distribution system. The distribution market operator is the distribution level equivalent of the market operator, which is responsible for managing the local electricity market and scheduling power transfers to achieve the optimal operation of distributed energy resources in the distribution system. The objective of this paper is twofold. The first objective is to look deeper into the distribution market operator and its role and interactions with other stakeholders in the so-called coupled local and central (national) market model. The second objective is to investigate quantitatively the distribution market operator's bidding strategies in the central day-ahead market and the effect of different bidding strategies on the performance of the local market. For this, distribution market operators dealing with uncertainties within the central day-ahead market prices is studied through two main approaches; the scenario-based approach to minimize the expected cost and the min-max regret as a robust risk-averse approach. The results from these two approaches are compared with the deterministic and perfect knowledge approaches as limits of the worst and best performances of the distribution market operator, respectively. Results show that the approach that the distribution market operator will choose is a trade-off between being risk-averse, having a higher amount of energy generated from distributed energy resources, and finally a lower system's cost.

A multi-block ADMM based approach for distribution market clearing with distribution locational marginal price

This work investigates a distribution locational marginal price (DLMP) scheme for promoting the market penetration of small-scale prosumers/consumers connected at distribution level and boosting their potential of demand response. In the distribution market, the distribution market operator (DMO) negotiates with each participant a node-specific DLMP and the respective demand profile of customer, so as to maximize the social welfare while respecting the operational constraints of the distribution system and load devices. First, different customers at demand side are modeled, including prosumers with photovoltaic (PV) generation, distributed storages and electric vehicles. All of them communicate with DMO and realize automated demand response by intelligent trading agent. Then, the distribution market clearing model for a massive number of customers in an ex-ante trade scheme is formulated as a market-based multi-period alternating current optimal power flow (ACOPF). As this clearing model is separable, the augmented Lagrangian relaxation (ALR) method is adopted to solve the market clearing model in a parallel manner. In the ALR, a second-order penalty term at each node is introduced to ensure the convergence, whereas it is not separable for customers. Hence, there are a multi-block demand variable coupled within the second-order penalty term at each node. A standard alternating direction method of multipliers (ADMM) algorithm cannot converge in this situation. To tackle this multi-block problem, we exploit the multi-block ADMM and distributed Dauglas-Rarchford Splitting method (d-DRSM), both of which proximally regularize the customer-specific subproblem to ensure global convergence. Furthermore, the prediction-correction mechanism in d-DRSM algorithm can achieve better rate of convergence in comparison with the multi-block ADMM. Finally, case studies are conducted and numerical results have shown the effectiveness of the proposed methodology, and the performance of different algorithms are analyzed for improving the clearing method’s efficiency.

Optimal power management of dependent microgrid considering distribution market and unused power capacity

This study presents an optimal power management for a microgrid (MG) in distribution market environment. The MG operator is able to have interactions with the distribution market operator (DMO) and adjacent MG (AMG) operator to supply its local loads. The DMO regulates the electricity market, and assigns the electricity price and power profile for the MG. The motivation behind the use of The DMO is that it works as an entity between MG and independent system operator (ISO) in order to guarantee a flexible operation by reducing power fluctuation and reduce the unintentional peak loads in low market price hours. The unused power capacity in AMG is used during the islanded hours through an additional interconnection point connected to the MG. Using this method reduces the need for installing new generation resources, which will be a practical and economical solution for MG developer. This MG which is able to provide power from distribution market and the AMG with two interconnection points is named dependent MG (DMG). A market-based stochastic model containing distribution market constraints and AC power flow formulations employs conditional value at risk (CVaR) methodology to capture the loads and wind uncertainties.The effectiveness of the presented model is evaluated on a 20 kV test system using different case studies. In this test system, the operator is the owner of all generation units. The numerical analysis explicate that presented model reduces the operation cost of DMG with the aim of responsive loads, unused power capacity, energy storage system (ESS) and power generation units. The market-based scheduling also provides operational flexibility for distribution system by adjusting flexibility limit of market constraints. It is also shown that changing risk preferences level changes the power generation pattern in ESS and causes a costly operation of resources in risk-averse strategy. The competence of this model is significant when the preventive maintenance (PM) program is carried out, and the DMG should rely on its flexible resources and AMG’s available power capacity, which could reduce the load shedding.

Enabling market participation of distributed energy resources through a coupled market design

In this study, a new market model is proposed to enable the smooth integration of distributed energy resources (DERs) in day-ahead (DA) and balancing (BL) markets. The DA market is a joint energy and reserve market. DERs participate in the local market operated by the distribution market operator (DMO) which is coordinated with the wholesale market. During local market clearing, the DMO considers the technical constraints of the distribution grid to keep the operation of the distribution grid within limits. Moreover, the DMO, through preliminary scheduling, estimates preliminary prices for energy, reserve and balancing services. These can be seen as offer prices by which the DMO will bid into the TMO DA and BL market. The uncertainty of renewable-based DERs is considered through the use of stochastic programming. The proposed market model is compared with a centralised market model. Results show that adding the distribution network constraints to the proposed market model is capable of alleviating overloading and appropriately accounting this effect in terms of increasing the operational cost. Moreover, it has been shown that the scalability of the proposed market model is higher compared to the currently applied centralised market models.

A Data-Driven Stackelberg Market Strategy for Demand Response-Enabled Distribution Systems

A data-based Stackelberg market strategy for a distribution market operator (DMO) is proposed to coordinate power dispatch among different virtual power plants, i.e., demand response (DR) aggregators (DRAs). The proposed strategy has a two-stage framework. In the first stage, a data-driven method based on noisy inverse optimization estimates the complicated price-response characteristics of customer loads. The estimated load information of the DRAs is delivered to the second stage, where a one-leader multiple-follower stochastic Stackelberg game is formulated to represent the practical market interaction between the DMO and the DRAs that considers the uncertainty of renewables and the operational security. The proposed data-driven game model is solved by a new penalty algorithm and a customized distributed hybrid dual decomposition-gradient descent algorithm. Case studies on a practical DR project in China and a distribution test system demonstrate the effectiveness of the proposed methodology.

Market-Based Versus Price-Based Microgrid Optimal Scheduling

An optimal scheduling model for a microgrid participating in the electricity distribution market in interaction with a Distribution Market Operator (DMO) is proposed in this paper. The DMO administers the established electricity market in the distribution level, sets electricity prices, determines the amount of the power exchange among market participants, and interacts with the Independent System Operator (ISO). Considering a predetermined main grid power transfer to the microgrid, the microgrid scheduling problem will aim at balancing the power supply and demand while taking financial objectives into account. Numerical simulations exhibit the application and the effectiveness of the proposed market-based microgrid scheduling model and further investigate merits over a price-based scheme.

Learn from the Past: It's Better Than Repeating It [In My View

The articles in this issue of the publication present a contrast in the outlook for the grid of the future. On one hand, serious efforts are underway to define the distribution system operator (DSO ) and plan the transition from today's distribution utility to tomorrow's distribution market operator. On the other hand, a grim assessment of the cyber insecurity of the grid operating-system infrastructure is, if anything, understating the risks and challenges in addressing them.