Different types of electricity markets modelled using PLEXOS Integrated Energy Model The UK Balancing Market example

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1 Different types of electricity markets modelled using PLEXOS Integrated Energy Model The UK Balancing Market example Peny Panagiotakopoulou, Senior Power Systems Consultant, Energy Exemplar Europe Overview of different electricity markets Electricity is a commodity that cannot be stored economically in large quantities. Therefore, generation must equal demand plus energy lost as heat when electricity is transported (transmission / grid losses) on an instantaneous basis. Otherwise the grid frequency starts deviating from its reference value, which can result in a system collapse. The design of electricity markets is adapted to deal with this particular property of electricity. Different types of electricity markets are arranged in a sequential order, starting years before the actual delivery and ending after the actual delivery. Figure 1: Temporal ordering of the different electricity markets (source: KU Leuven Energy Institute) It is the Transmission System Operator (TSO) who needs to ensure that the instantaneous demandsupply balance is maintained. Before the actual delivery, the balance responsibility is passed on to Balance Responsible Parties (BRPs). A BRP is a private legal entity that takes up the responsibility to compose a balanced portfolio. Forward and Future Market The Forward & Future Markets operate from a year or more ahead down until the market closes at a time defined as Gate Closure when the System Operator takes on the role of residual balancer. Forwards and futures are contracts for firm delivery of electricity at a certain time in the future for a price agreed upon today. Futures are standardized contracts that can be further traded on power exchanges. Forwards are mainly traded bilaterally over-the-counter and are not standardized, giving more flexibility to the involved parties, and they are usually not further traded. 1 P a g e

2 Day Ahead Market The Day-Ahead Energy Market (DAM) is a financial market where market participants purchase and sell energy at financially binding day-ahead prices for the following day. The DAM market is usually based on the bid (Buy) and offer (Sell) data submitted to the DAM by the market participants. Day-Ahead markets provide a forward market to hedge against the spot price volatility. The buyers and sellers can be large energy users (buyers), distribution retailers/utilities (buyers), power plant owners (sellers), or financial traders (buyers and sellers). Day-Ahead markets are generally used in conjunction with real-time (spot) markets to balance how market participants deviate from their dayahead energy positions. In the UK electricity is mostly traded through exchanges (APX and N2EX) to match offers and bids of participants. APX Power UK (established in 2000) offers a market place for integrated trading, clearing and notification for spot and prompt power contracts and a trading platform for cleared forwards contracts. Intra Day Market In the Intra-Day Market, electricity is traded on the delivery day itself. The intra-day market enables market participants to optimise their position and correct their day-ahead nominations due to better wind forecasts, unexpected power plant outages, etc. The GB Balancing Market In the GB Wholesale electricity market, organisations that require electricity for their customers (Suppliers), enter into contracts with organisations that produce electricity (Generators), sometimes through intermediaries called Non Physical Traders. These types of organisations are called BSC Parties. A BSC Party is any company that has signed the Balancing and Settlement Code Framework Agreement. This includes all licensed electricity companies in Great Britain who are required by their license obligations to sign the BSC. Suppliers will calculate the estimated electricity requirements for their portfolio of customers for each Settlement Period, and they will then enter into contracts with generators in order that their customers receive the correct quantity of electricity for each Settlement Period. This forms Forward bilateral contracts & power exchange markets for firm delivery of electricity, which operate from a year or more ahead down until the market closes at a time defined as Gate Closure when the System Operator takes on the role of residual balancer. Due to the fact that electricity cannot be stored, generation must always equal demand plus energy lost in the form of transmission losses. But, there are several challenges in achieving this, since the contracts between generators and suppliers do not always completely balance the Transmission System (i.e. suppliers may not always accurately predict demand; generators may not always be able to tightly control their generation - intermittent generation or plant tripping off the transmission system; problems may arise with transmission lines; the market trades in half hour Settlement Periods, but the Transmission System must balance at every instant). Where the Transmission System is not balanced it is called Imbalance. The transmission system should be always balanced, a job that is known as system operation, and is managed by the System Operator (SO), National Grid in the UK. In order to balance the Transmission System, the SO needs to know what generators intend to generate and what suppliers intend to consume for each Settlement Period. The SO needs this information before the start of the Settlement Period so that it can understand the Transmission 2 P a g e

3 System imbalance, plan how to balance it and take balancing actions. Therefore, generators and suppliers submit Physical Notifications (PNs) for each Balancing Mechanism Unit (BMU) to the SO for each Settlement Period. 1 hour before each Settlement Period the PNs of Parties are frozen. This is called Gate Closure. At this point the PNs become Final Physical Notifications (FPNs). After Gate Closure, Parties must try to adhere to the FPNs submitted to the SO. They should only deviate from their FPN at the instruction of the SO. Following Gate Closure, the SO is able to evaluate the net imbalance of the Transmission System; also called the Transmission System length. A Long' Transmission System is the one where there is more generation than demand, whereas a Short' Transmission System is the one where there is more demand than generation. Parties submit notices, called Bids and Offers, telling the SO how much it would cost for them to deviate from their Final Physical Notification. The SO assesses all the Bids and Offers for each Settlement Period and chooses the ones that best satisfy the balancing requirements of the Transmission System. The SO is trying to balance the Transmission System as efficiently as possible. So ideally the SO would choose the cheapest balancing action, one after the other. Nevertheless, this is not always possible since the SO should also consider any technical limitations of the Power Station (ability to increase or decrease generation/demand quickly enough to meet the requirement), and any technical limitations of the Transmission System (ability of the generation/demand to be transmitted to the part of the Transmission System where it is needed). Balancing Markets modelled in PLEXOS In a Balancing Market in PLEXOS, generators define their dispatch level from a day-ahead market using the properties Generator Offer Base/ Price. The goal of the balancing process is then to select optimal increments ( incr ) and decrements ( decr ) around these day-ahead positions to meet deviations in the real-time load. The input pairs Generator [Offer Quantity] and [Offer Price] are used to define the incr and decr offers. Positive quantities are interpreted as offers to increment the generator's dispatch above the base quantity, while negative quantities are interpreted as offers to decrement the generator's dispatch. The impact on the system is the product of the incr / decr and the offer price, thus an incr with a positive price increases cost accordingly and a decr with a positive offer price, decreases system cost. PLEXOS Methodology Balancing Market modelling in PLEXOS is usually undertaken in two stages. At Stage 1 the Day-ahead market is modelled using hourly time intervals, while the results for generation, un-dispatched capacity, available capacity, SRMC, etc. of the generating units are saved in Data Files. Stage 2 then follows, which is considering the Real-time market, and uses finer resolution, i.e. time intervals of 30 or 15mins. In addition, the previously saved results of Stage 1, as well as a new forecast of demand and/ or renewables are used by PLEXOS, in order to optimise the balancing of units. The solution of stage 1 model (i.e. Base DA in this case study) can act as input for the subsequent stage 2 model. The data are passed between simulations simply be enabling the option to Write Flat Files in the Report object. In the Day-ahead model the user can select all the outputs that will act as inputs to the Real-time 3 P a g e

4 model to be written in flat files, and these will then be written into a folder structure. These files can then be referenced in order to be used as inputs for the subsequent stage 2 model (i.e. Base RT in this case study). Figure 2: Data File objects used in the PLEXOS RT model The Day-ahead GB market model can be defined in PLEXOS as a common unit commitment and economic dispatch problem, where the objective function would be to minimize the total system cost in order to find the optimal on/ off decisions for the generating plants. Therefore, the Generator objects that participate in the GB market, are modelled using parameters such as their Max Capacity, Min stable level, Heat Rate, Fuel prices, VO&M Charge, FO&M Charge, Run Up/ Down rates, as well as random outages and maintenance events. Other system constraints (i.e. fuel or emission limits) as well as reserve requirements can be taken into account. For the Real -time GB market model (or the Balancing model) the Generator input properties are slightly modified, so as to consider the dispatch level from the Day-ahead market and create a bidding strategy around these day ahead positions in order to meet deviations in the Real-time demand and/ or changes in the renewables forecast. Figure 3: Generator properties used in the PLEXOS RT model The Day-ahead and Real-time models can then be run in batch mode: once the stage 1 model completes, the selected results will be passed as inputs to the stage 2 model. A potential inaccuracy though with the sequential setup is that the day-ahead decisions do not reflect the outcome of the real-time in that the initial conditions for generation and storage (in particular) for 4 P a g e

5 each day of the "day-ahead" model are based on the previous day-ahead solution not those from the real-time. PLEXOS deals with this issue through a more sophisticated way to link the solutions from one model to another. The Interleaved run mode addresses this by running both "DA" and "RT" Models interleaved, with initial conditions being passed back automatically from "RT" to "DA". Figure 4: PLEXOS Interleaved run mode The Day-ahead model can pass unit commitment and any other required information to the Real-time model so as to cover the first day of the simulation. Once the Real-time model has completed the first day it will pass initial conditions for Generator and Storage objects back to the Day-ahead model. The resolution of the models does not need to be the same e.g. Day-ahead model might run with hourly resolution, whereas Real-time model might be at a 30-minute resolution. PLEXOS Results For the purpose of this study the GB market model, defined with all GB generators capacities, fuel prices and GB demand for 2013, was used in order to setup the DA model. For simplicity purposes in this example, the Wind generators profile was kept the same between the day-ahead and real-time models. However, a deviation between the day-ahead (DA) demand and realtime (RT) demand was used. So, the GB Load 2013 data were used in order to form the DA demand scenario, whereas the RT demand scenario was formulated using samples created from these data acting as an expected profile with an error standard deviation of 5% (Figure 5). Figure 5: GB Load 2013, DA vs RT markets 5 P a g e

6 The Interleaved Collection is used in order to link the DA and RT models (Figure 6). Multi-level interleaved runs, of 3+ models, are also possible with PLEXOS. The user can setup for example 3 models to run in interleaved mode by linking the Day-ahead (DA) with Intra-day (ID), and Intra-day with Real-time (RT) models accordingly. The DA run will request the initial conditions from ID, but that will then request the information from RT, and therefore the data is essentially passed down, all the way from RT to DA. Figure 6: Models Setup in Interleaved Mode Real-time market clearing prices, also known as balancing energy prices, represent energy prices that reflect deviations of forecasted versus actual load and generation. By their nature, real-time prices are generally much more volatile than day-ahead prices. Typically, wind influenced negative prices are reported based on Real-time market outcomes. However, Real-time power prices can impact both Day-ahead clearing prices and bilateral power purchase agreements, as they can influence pricing expectations for contract negotiations. Figure 7: GB Daily Prices, DA vs RT markets run in Interleaved Mode (2013) 6 P a g e

7 Running the models in batch mode; where the DA model is followed by the RT model, results in a Cost to Load value for the RT model, higher than the one received when in Interleaved Mode. Parent Name Collection Child Name Category Datetime RT Batch RT Interleaved Cost to Load ($000) Cost to Load ($000) System Region GB - 01/01/ System Region GB - 01/02/ System Region GB - 01/03/ System Region GB - 01/04/ System Region GB - 01/05/ System Region GB - 01/06/ System Region GB - 01/07/ System Region GB - 01/08/ System Region GB - 01/09/ System Region GB - 01/10/ System Region GB - 01/11/ System Region GB - 01/12/ Sum Figure 8: RT GB Monthly Cost to Load, Batch vs Interleaved Mode (2013) The Region Cleared Offered Cost would represent the cost of Generation as seen by the Market Operator, and is calculated as: Cleared Offer Cost = ( b ) ( Offer Price b Offer Cleared b ) + Offer No Load Cost Units Generating Figure 9: GB Cleared Offer Cost (half-hourly results) 7 P a g e

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