Virtual Power Plant Market Participation

Published Date: 02 Nov 2017

Abstract— the deregulation of electricity markets enables flexible and efficient framework for generation companies to trade electricity in a competitive environment. New policies and incentives stimulate increased penetration of environmentally friendly resources. These caused the presence of more small and medium scale renewable generation units distributed all over the grid. However the variable nature of renewable energy sources and the lack of their centralized monitoring create challenges for the system operation affecting the active power balancing efficiency. The idea of aggregating distributed energy sources is emerging to the concept of virtual power plant that enables their controllability and better representation to the system operator. In this paper we present a market framework enabling virtual power plant to participate in energy markets with joint offerings of generation and demand response services. Internal and external trades are enabled by introduction of two market layers. The developed model maximizes the virtual power plant profit with respect to the forecasted power outputs of variable generation units and provides optimal demand response pricing scheme.

Index Terms—Virtual Power Plant, Demand Response, Distributed Generation, Dynamic Pricing, Stochastic Programming.

Introduction

The restructuring of power systems created an admirable environment for energy producers to trade electricity in competitive markets. Various policies and incentives like feed in tariff schemes facilitate installations of more renewable energy sources (RES) and distributed generation (DG). This tendency can cause grid stability issues mainly because of the variable nature of some RES (Calvert). One of the promising solutions for these is the concept of virtual power plant (VPP) which basically acts as an aggregator of DGs and RES. VPP performs monitoring and control of all available energy resources and from the transmission point of view makes them visible as a single conventional power plant (You et al. 2009).

A VPP enables flexible market participation for DG owners by representing them in the wholesale market. Most importantly individual DGs become visible and controllable units to system operators, maximizing their possible profits and operational flexibility (Management 2009).

Generally generation companies can establish long term agreements with their consumers on the basis of bilateral contracting. These types of contracts include pre-specified conditions such as amount, time, duration and price of power delivery(Anon.). Similar to a conventional generation company a VPP can also incorporate loads and offer them long term bilateral contracts. In addition a VPP can enable loads to actively participate in the energy trades by subscribing them for demand response (DR) programs. In this way VPP also becomes a load aggregator that is able to provide or use an aggregated portfolio of DR services.

In this paper we propose a combined aggregation of DR and DGs forming a single energy profile aggregated in a VPP. We establish an internal market between VPP and the bilaterally contracted consumers enabling them to offer demand reductions via price signals. An optimal dynamic DR pricing scheme is developed to allow VPP to buy reductions internally. In this way, in the wholesale market the VPP trades only its surplus generation which depends on three factors: (1) levels of RES outputs, (2) internal demand, and (3) performed internal load reductions. The reduction levels in their turn depend on the DR pricing rates interlinked with the demand price elasticity level. The proposed VPP model deals with these interdependencies and provides optimal DR pricing together with the scheduling of generation units.

Various VPP structures are proposed in the literature including a combined aggregation of both loads and DGs or RES (Pandžić, Kuzle, and Capuder 2013),(Mohammadi, Rahimi-Kian, and Ghazizadeh 2011),(Zdrili and Constants 2011). In the study made in [6] the flexible loads are covering the uncertainties of variable output of wind generation within a VPP. Demand side participates in the energy market through a DR program similar to DADRP currently offered by NYSIO (Anon. 2003). In [5] the VPP is aggregating various renewable and conventional DGs providing power for bilaterally contracted loads and selling the generation surplus to the market. The load is not considered to be flexible and is modeled to be fixed over one week period of time. The VPP flexibility is enabled by aggregation of storage system which deals with the uncertainty of RES outputs. The cost of such storage systems was not taken into account; however they have assessed the value of different storage capacity levels. Similar approach is done in [7] with a VPP profit maximization model incorporating DGs and fixed load.

Some other papers are focusing on VPPs that incorporate only loads. In this way VPP acts as a load aggregator and for instance (Management 2009) provides only real time DR services to the grid. They have proposed a building modeling framework which enables them to control thermostats of their consumers. One critical assumption made in [3] is the fact that all thermostats are assumed to be "on" at all times before the control actions are performed. However they mainly focused on performing demand curtailments in a way that minimizes the discomfort of the DR participants and maximized the profit of VPP at the same time.

[], [] and [] are propose VPP models for market participation having both generation and flexible load at their disposal.

The main innovation of this work is the enabled internal daily DR trading between VPP and the aggregated load. The developed model outputs optimal DR pricing and maximizes the expected VPP profit.

The paper is divided into five main sections. Section II describes the system architecture and the nature of interactions between all participants. Section III presents the optimization model formulation. Section IV applies the model to a case study. Section V summarizes the main conclusions drawn from the study.

Market Interactions

The proposed VPP consists of solar and wind RES plus the conventional power plant at its disposal which is used as a backup unit. VPP also aggregates bilaterally contracted load which is enabled to participate in energy trades by offering demand reductions in response to price signals sent by the VPP operator (VO). In this way there are two layers of market interactions: Load-VO and VO-ISO.

Fig. . Financial and energy flows between market participants

On the level of ISO the VPP is trading the surplus generation and buys power in case of internal deficit. The VPP is required to satisfy the internal demand at all hours for the contracted price. On the other hand VPP consumers are enabled to offer demand reductions in response to DR hourly λDR prices provided by the VO. Figure 1 shows all power and financial flows between participants including VPP consumers and ISO.

All interactions are happening in a day-ahead market. The deviations of RES outputs in the real time market are out of the scope of this research. For that reason the VPP forecasts only the day-ahead market clearing prices and excludes the real time spot prices.

Introduction of two market layers allow the VPP to offer higher generation offerings at hours for which reductions are expected.

Demand Response Model

The proposed DR scheme is similar to demand reduction bidding programs offered by NYSIO and PGE (Anon. 2003)-(Cherry 2008). The main difference is that the VPP is buying the reductions only internally on a day-ahead basis. In this way the loads are called for reductions every day. DR price signals sent to consumers mainly depend on the levels of expected hourly market prices and the projected total demand elasticity. These hourly prices of DR are contracted in a way that VO is always ready to buy back the energy that it has to supply to the end users. So the reduction prices are lying between bilateral price and the forecasted market prices. These mean that there should be an optimal DR price for every hour that maximizes the VPP profit.

Demand elasticity of the VPP system at any hour is assumed to follow the exponential relationship towards DR pricing as it is shown in (1).

(1)

∆ is the difference between DR price and the market price expected to be cleared by the ISO . ⍺ is the elasticity coefficient and D is the projected demand.

(3)

The DR pricing limits are the following:

(4)

Fig. 2 Demand Reduction Elasticity

Figure 2 shows the relationship of demand side elasticity and the limits of DR pricing. The maximum limit for a reduction is the expected total demand of that hour.

On the individual consumer scale an important factor is the baseline estimation and the proper measurement of performed reductions. This is done with the similar approach as described by PGE Demand Reduction Bidding (DRB) program [9]. The baseline of an individual consumer is determined by calculating the hourly energy usage of the prior two weeks. Stochastic programming is applied to set higher weights to the same weekdays of the demand.

So the baseline demand of a consumer i is estimated as follows:

(7)

where k indicates the day and the corresponding probability of consuming the same amount.

The days for which reductions have been performed are excluded from the calculations.

The VPP profit with respect to DR pricing is determined as follows:

(8)

(9)

The profit maximization given the elasticity coefficient is performed as follows:

(10)

The optimal can be easily derived from (10):

(11)

The optimal price will always be between and in all cases when. To normalize the range of optimal pricing and identify the direct dependency on the demand elasticity factor we derive the following parameter:

(12)

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Fig. 4 Demand Reduction Elasticity

Figure 3 shows the relationship between and different levels of elasticity factor. As it is shown the DR price can’t exceed the middle point of difference between market price and the bilaterally contracted price. To achieve certain amount of reduction the VPP operator will have to set higher price when the elasticity factor is low and vice versa. The major drawback from this is the fact that the price incentive decreases as the elasticity factor increases. Which means that in a long-run this interdependency will cause the DR price to oscillate over time.

Fig. 6 Interdependency between DR price and elasticity factor

It is important to note that the obtained results of DR pricing are separated from the other constraints of the VPP. The results will vary in case of interlinking DR and VPP constraints together.

VPP Model

Assumptions

The developed model simulates a VPP operation and performs the optimal scheduling of energy resources. VPP includes RES and flexible load divided on three categories: (1) household, (2) commercial and (3) industrial consumer sectors. Also VPP owns a conventional power plant on its disposal as a backup energy source.

The power output of RES units is assumed to be well forecasted. The demand of flexible internal load is given and pre-specified by the bilateral contract conditions.

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