Ramping ancillary service for cost-based electricity markets with high penetration of variable renewable energy

Journal Pre-proof Ramping ancillary service for cost-based electricity markets with high penetration of variable renewable energy ´ ´ Diego Godoy-Gonzalez, Esteban Gil, Guillermo Gutierrez-Alcaraz

PII:

S0140-9883(19)30351-2

DOI:

https://doi.org/10.1016/j.eneco.2019.104556

Reference:

ENEECO 104556

To appear in: Received Date:

26 June 2018

Revised Date:

11 October 2019

Accepted Date:

16 October 2019

´ ´ Please cite this article as: Diego Godoy-Gonzalez, Esteban Gil, Guillermo Gutierrez-Alcaraz, Ramping ancillary service for cost-based electricity markets with high penetration of variable renewable energy, (2019), doi: https://doi.org/10.1016/j.eneco.2019.104556

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Ramping ancillary service for cost-based electricity markets with high penetration of variable renewable energy Diego Godoy-Gonz´aleza , Esteban Gila,∗, Guillermo Guti´errez-Alcarazb

a Universidad

Tecnol´ogico de Morelia, Av. Tecnol´ogico 1500, Morelia 58120, Mexico

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b Instituto

T´ecnica Federico Santa Mar´ıa, Av. Espa˜na 1680, Valpara´ıso, Chile

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Abstract

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System operators and electricity market stakeholders are facing new challenges related to increasing variability in both generation and demand. Rising generation levels of variable renewable energy (VRE) sources cause fluctuations in net loads that need to be met in real time by the rest of the system resources. While in some bid-based markets ancillary services have been proposed to procure ramping in an economic and efficient way, cost-based markets are struggling to define products and mechanisms that can accommodate to their rules, so as to incentivize and properly remunerate their procurement. In this paper we conceptualize a Ramping Ancillary Service (RAS) for cost-based electricity markets with high penetration of VRE. The proposed RAS scheme is evaluated through simulations of the Chilean electric system. Numerical results show that it can be implemented in cost-based markets with positive technical and economic results, and that it may be a suitable solution to alleviate net load changes caused by high penetration of VRE.

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Keywords: Ancillary services; ramping; uncertainty modeling; electricity markets; flexibility; variable renewable energy; market design

Gil. Tel.: +56-32-2654-395. E-mail address: [email protected]

∗ Esteban

Preprint submitted to Elsevier

October 11, 2019

Nomenclature Sets and indexes t Time index t = 1, 2, . . . , N − 1 defining 5-minute intervals of length ∆t i ∈ GRAS RAS resource index, in the set of Ramping Ancillary Service (RAS) providers GRAS j ∈ GVRE Generator index, in the set of Variable Renewable Energy (VRE) generators GVRE

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Stage 1: Day-ahead bidding and Day-Ahead Unit Commitment (DAUC) BidQRU RAS up bid quantity made by resource i [MW] i BidPRU RAS up bid price made by resource i [MW] i Day-ahead preliminary RAS up requirement for interval t of next day [MW] ReqRU,pre t UCi,t Commitment status for RAS resource i in interval t of next day (binary variable) GenUC Programmed dispatch in DAUC of RAS resource i in interval t of next day [MW] i,t max Pmin Generation limits of resource i [MW] i , Pi CapRU,pre Programmed ramping capability of resource i in interval t of next day [MW] i,t RUimax Technical RAS up capability limit of resource i [MW]

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Stage 2: 5-minute ahead RAS procurement up S upplyRU in interval t as a function of quantity X [US D/MW] t (X) Supply function for RAS RU Capi,t Real ramping capability of resource i in next interval interval t [MW] VoRS RU Value of RAS up reserve shortage [US D/MW] ReqRU RAS up requirement estimated for next interval t [MW] t prog NDt Expected net demand in interval t [MW] Requnc,up RAS requirement due to underestimation of net load forecast in interval t [MW] t Requnc,down RAS requirement due to overestimation of net load forecast in interval t [MW] t

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Stage 3: RAS provision in real time operation RUtprog Total RAS up requirement in t, estimated in period t − 1 [MW] real RUi,t RAS up actually delivered by resource i in interval t [MW] NDreal Actual net demand in interval t [MW] t GenVRE Actual dispatch of VRE generator j in interval t [MW] j,t S RUtreal Sum of changes causing ramping up needs (load and VRE generation) in t [MW] Genreal Actual dispatch of RAS resource i in interval t [MW] i,t

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Stage 4: Remuneration and payments MCtenergy Marginal energy cost for the system in interval t [US D/MWh] MCtRU Marginal RAS up cost for the system in interval t [US D/MW] VCi,t Variable cost of resource i in interval t [US D/MWh] RemRU RAS up remuneration of resource i in interval t [US D] i,t RU U pli f ti,t Uplift RAS up payment for resource i in interval t [US D] PayRU RAS up payment by participant j in interval t [US D] i,t

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Caveats about notation: Only variables for ramping up are explicitly defined in the nomenclature. Those related to ramping down are similar, but replacing RU by RD. If a variable includes the subscript t, it refers to its value in a specific interval. DAUC variables use the superscript pre, and others may sometimes have subscripts such as load or total. If so, its meaning will be provided in the context. Variables with a more limited scope are defined when used. Caveat about units: Ramping is usually measured in [MW/min]. However, for simplicity and to facilitate its understanding in the context of other variables, we will refer to it in terms of [MW], understood as the dispatch change from one 5-minute interval to the next. 2

1. Introduction

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Electricity market designs are evolving to accommodate a growing share of variable renewable energy (VRE) generation while ensuring power system reliability, economic efficiency, incentive compatibility, and fairness [1]. Due to the characteristics of their natural resources, solar and wind generation introduce variability and uncertainty in the net load [2]. Solar generation causes a positive net load ramp when the sun sets, and a negative net load ramp when it rises, leading to the well known ‘duck curve’ [3]. Wind generation depends on uncertain wind flows and currents, conditioned by the characteristics of wind farm sites [4, 5]. To account for the variability of these VRE sources, generators and independent system operators (ISO) use prediction models before scheduling generation [6]. Forecasting errors, however, add uncertainty and may increase even further the ramping requirements of an electric power system [7, 8].

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As net load variability and uncertainty caused by VRE keeps increasing, power systems require more flexible generation or responsive demand resources to balance the load [9, 10]. If a system cannot procure additional flexible resources to meet this need, it must carry more spinning reserves and sometimes force increased cycling of thermal units, VRE curtailment, and load shedding, among others. Another important issue occurs for low net load conditions [11]: if inflexible baseload generators are the ones supplying the net load, they may be unable to rapidly change their dispatch when an unbalance occurs. This may force the start of an expensive peaking generator that will be setting up the marginal price for the whole system, unduly increasing revenues of the same generators whose inflexibility (in the case of baseload units) or variability (in the case of VRE generators) caused the problem in the first place. Furthermore, as stated in [12], misallocation of incentives in low-carbon power systems may lead to a mix of resources unable to satisfy the underlying system’s needs, concealing the true value of storage and flexible demand. Hence, flexibility requirements necessitate a special market design capable of promoting innovation and deployment of new technologies [13, 14]. All these drivers are encouraging the development of balancing ancillary service products capable of optimally matching generation and demand [15], while providing economic incentives for innovating and reducing the costs for reliability. A literature review of flexibility products and markets is presented in [16].

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In the United States, the California Independent System Operator (CAISO) [17] and the Midwest Independent System Operator (MISO) [9] are pioneers in the design and implementation of a bid-based Flexible Ramping Product (FRP, known as ‘flexiramp’ in CAISO and ‘ramp capability’ in MISO), and their markets provide the means to acquire sufficient net load ramping capacity through economic offers [18]. The economic, environmental, and reliability benefits of FRPs in MISO’s electricity markets are explored in [19], showing that they accomplish the objective of facilitating wind integration and reducing system costs. In [20], an initial evaluation of FRP design for real-time balancing markets is conducted, by adding FRP constraints to the ISO’s scheduling software and paying the shadow price of the constraints to generators helping meet the requirements. They show that a flexiramp product for real-time ISO markets could increase market efficiency, approaching the ideal of an expected cost-minimizing solution based on stochastic unit commitment. In [21], stochastic market clearing proves to be more effective in hedging against wind uncertainty than deterministic alternatives, reducing the spread between day-ahead and real-time prices by more than 40%. While stochastic dispatch models could, in theory, be used to endogenously determine the optimal level of ramping reserves, unlike FRP such models are not yet practical for large power markets and require further research. Participation in a FRP is not limited to conventional generators, as different authors have suggested 3

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that the service could also be profitably provided by battery energy storage [22], flexible demand [23], and even wind power generation [24]. All existing implementations of FRP (e.g. MISO and CAISO) are very recent and have so far occurred in bid-based electricity markets, where generators submit their supply curves as bids to a centralized auction cleared by the ISO for both energy and ancillary services. In contrast, many Latin American systems with large percentages of hydroelectric energy (e.g. Chile, Brazil, Peru, Bolivia, and countries in Central America) can be qualified as cost-based electricity markets. Here, the ISO audits all generation parameters (i.e. fuel costs, heat rates, hydro energy availability, minimum loads, ramping limits, and other operational constraints) to estimate the marginal costs on every bus, used to settle energy purchases and sales [25]. In this type of market, provision of ancillary services is usually mandatory, following ISO’s instructions, and not through a market mechanism. Although this type of design has been defended in the past (e.g. [26]), it has lately received a fair amount of criticism (e.g. [25]) due to the economic inefficiencies it may introduce. However, it is unlikely that countries with cost-based markets will change this paradigm in the near future. Therefore, studying how to encourage and properly remunerate the procurement of flexibility services in these types of markets is relevant, specially as some of them are rapidly adopting VRE technology to replace their aging conventional generation. The number of VRE generation projects in different stages of development suggest that the ramping needs in the Chilean National Electric System (NES, a cost-based market) might grow significantly in the next few years [27]. The northern portion of the NES, where the arid and high-altitude Atacama desert is located, makes it suitable for large amounts of solar generation [28]. Furthermore, the largest Chilean energy holdings have recently announced their decision to not longer build new thermal nor large hydro generators, and will be even mothballing some of the existing ones, addressing changes in both economic and sustainability paradigms. Although some of the future net load ramp needs could be met by hydro generation located in southern Chile, transmission limitations may create regional electricity sub-markets and local ramping needs. Additional flexibility could be obtained from the system’s conventional generation, although with significant additional operating costs due to cycling [29, 30]. Reference [31] identifies three main causes of additional costs due to ramping by conventional thermal generators: (i) Efficiency losses due to suboptimal operating points; (ii) higher operation and maintenance costs; and (iii) increased forced outage rate. Furthermore, cycling may increase emissions and the environmental footprint of electricity production [13]. Thus, ramping flexibility will require the development and/or modification of ancillary services market policies, the creation of incentives, or the imposition of new requirements on generators [30, 32]. Ancillary services is the most likely solution to deliver the necessary flexibility to electrical power systems [10, 33–36]. This paper proposes a Ramping Ancillary Service (RAS) suitable for cost-based markets. The main contributions of this paper are three. First, we discuss the formulation of a RAS for its use in cost-based markets and, particularly, the Chilean NES. Second, we propose a pricing, remuneration, and payment scheme for procurement of ramping services. Third, we evaluate through computer simulation the potential technical and economical benefits of the proposed ancillary service. The remainder of this paper is organized as follows. In Section 2, the conceptual formulation of an RAS is briefly discussed, based on principles from CAISO’s FRP. Section 3 details the processes and formulations for the procurement, operation, and market settlement of the proposed RAS. Section 4 presents the test system and the cases to be evaluated. Section 5 presents market simulation results used to conduct a technical and economic evaluation of the scheme. Section 6 presents the main conclusions of the paper and discusses directions for further research. 4

2. Guiding principles for the formulation of a Ramping Ancillary Service (RAS)

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In this section we briefly discuss the conceptual formulation of an RAS, using the FRP as proposed and adopted by CAISO as a base. In this paper we do not present details of ramping requirement estimation, resource operational limits, or changes to objective function, and constraints of the market optimization processes, as the guiding principles and respective equations are similar to those in [18, 37]. Conceptually, an RAS must procure ramping capability or flexibility for its eventual use in a future interval. This characteristic distinguishes it from frequency regulation and spinning (and non-spinning) reserve services, which manage system contingencies in the same time interval in which they occur. Furthermore, spinning (and non-spinning) reserve services can only contribute to ramping-up requirements, while an RAS, similarly to CAISO’s FRP, can contribute to both up and down ramping [36, 37]. Due to technical constraints, the ISO may not have sufficient resources to balance generation and load in real time. If that happens, it may be forced to shed load, curtail VRE generation, or to rely on frequency regulation services to correct the power imbalance. In turn, this has negative consequences both in terms of economic efficiency and reliability: difference between the dayahead programs and real-time operations may increase, generation units may be dispatched outof-merit, market efficiency may decrease, and operational security may deteriorate as a result of insufficient reserves. Thus, the main purpose of an RAS is to ensure beforehand that enough ramping capacity is available in the system while allocating economic incentives efficiently. In general, an RAS must be designed to manage both the variation and uncertainty in the system’s net load and ensure that the balance between generation and demand can be accomplished at minimum cost when ramping limits of generators are considered. Thus, total ramping requirements (either up or down) for a specific time period have 2 components, one covering net load variability and the other one uncertainty of the net load forecast. The first one is related to the expected variation of net load between two consecutive intervals as a results of shifting VRE generation and electric demand patterns. The second one is associated to net load forecast uncertainty. In CAISO, FRP, energy, and other ancillary services are co-optimized both in the real-time unit commitment (RTUC) and the real-time dispatch (RTD), with 15-minute and 5-minute clearing intervals respectively. In both optimization problems, FRP is modeled as constraints representing ramping capability. Both the ramping availability and energy schedules in RTUC are binding at the 15-minute market price. Only resources that have submitted bids in this market and can be dispatched in RTD can obtain a flexible ramping award in excess of their forecasted movement for the interval [18]. However, generally cost-based markets lack RTUC and RTD processes. Practical implementation of a ramping ancillary service in these types of markets would require frequent estimation of future ramping needs and the procurement of enough flexible generation (or load) resources. Thus, for this purpose we propose adding 5-minute ahead ramping requirement estimation and ramping capacity procurement processes, in the form of an intermediate resource scheduling stage between Day-Ahead Unit Commitment (DAUC) and real time operations, as discussed in Section 3.1. Of course, the intervals’ length could be defined differently by the ISO without loss of generality (e.g. 10-minute intervals). In terms of potential providers, at the beginning ramping needs could be satisfied by incumbent generators. For this to happen, they could be encouraged to offer the service by, for example, increasing their chances to be committed. The remuneration mechanism should at least pay them for the cycling costs associated to providing the service. However, eventually the creation of a 5

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market for flexible ramping should encourage innovation and deployment of new technologies better suited to provide the service. In this sense, emerging technologies such as highly flexible gas-fired power plants, energy storage systems, and demand response are ideal candidates. The intended purpose of designing a RAS is that baseload energy and balancing services operate in separate markets and with different prices, encouraging economic efficiency through the proper allocation of incentives and payments. This guiding principle would also benefit electricity consumers, as economic efficiency in a market leads to a reduction of costs and prices. Therefore, the market settlement mechanism for a RAS should not only ensure that providers can recover their investments, but also to deliver value for money so that consumers benefit from the market design with lower prices and increased reliability [12].

3. Design of a Ramping Ancillary Service (RAS) for cost-based markets 3.1. Timeline for the procurement of RAS

Day-ahead reliability unit commitment

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RAS bidding & requirement estimation

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Unlike systems like CAISO, the Chilean NES and most cost-based markets only conduct a Day-Ahead Unit Commitment (DAUC) to program the operation of generation units for the next day. Therefore, there is not currently RTUC or RTD optimization. The diagram in Fig. 1 illustrates the suggested timeline for the procurement of ramping services in the Chilean market. First, participants capable of providing the service (e.g. generation assets, energy storage systems, and demand response) submit their bids the day before, which are constant for all 5-minute intervals of the following day. Also, ramping requirements for the system can be preliminarily assessed. Next, DAUC is conducted by the ISO to define generation resources to be committed the next day, ensuring that enough ramping capacity is available to satisfy day-ahead estimated requirements. The next day, in real-time operations, the ISO will regularly check the ramping needs for the next 5-minute interval and clear the RAS market based on the supply curve defined by the previous day’s bidding process.

Day d-1

Settlement of RAS bids in 5-minute intervals

Day d

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Figure 1: Suggested timeline for the procurement of RAS

The proposed scheme is conceptually separated in four stages, as Fig. 2 illustrates. The rest of this section follows the same structure. Subsection 3.2 discusses the bidding process occurring the previous day, the day-ahead estimation of RAS requirements, and the RAS constraints that need to be added to the DAUC. Subsection 3.3 discusses the procurement of RAS services in the 5-minute interval previous to the real operation. Subsection 3.4 addresses actions taken in real time and the actual usage of ramping resources. Finally, Section 3.5 discusses the RAS market settlement, including calculation of prices, remunerations, and payments for ramping services. 6

Estimation of RAS requirements for every interval of next day

ሼ𝐵𝑖𝑑𝑄𝑖𝑅𝑈 , 𝐵𝑖𝑑𝑃𝑖𝑅𝑈 ሽ ∀𝑖 ሼ𝐵𝑖𝑑𝑄𝑖𝑅𝐷 , 𝐵𝑖𝑑𝑃𝑖𝑅𝐷 ሽ ∀𝑖

𝑅𝑈,𝑝𝑟𝑒

𝑅𝑒𝑞𝑡 ∀𝑡 𝑅𝐷,𝑝𝑟𝑒 𝑅𝑒𝑞𝑡 ∀𝑡

Day-ahead unit commitment (with RAS constraints) ∀𝑡 = 1, … ,720

𝑆𝑢𝑝𝑝𝑙𝑦𝑡𝑅𝑈 (𝑋) 𝑆𝑢𝑝𝑝𝑙𝑦𝑡𝑅𝐷 (𝑋)

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Updating RAS requirements for next interval 𝑡

Defining RAS supply functions for next interval 𝑡

Stage 3: Real-time operation

Stage 2: 5-min ahead RAS procurement

Stage 1: Day-ahead program

RAS bidding for next day

𝑅𝑒𝑞𝑡𝑅𝑈 𝑅𝑒𝑞𝑡𝑅𝐷

Real operation Realization of uncertainties and RAS provision

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𝑟𝑒𝑎𝑙 𝑅𝑈𝑖,𝑡 𝑟𝑒𝑎𝑙 𝑅𝐷𝑖,𝑡

Stage 4: RAS market settlement

Calculation of:

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 RAS prices: 𝑀𝐶𝑡𝑅𝑈 , 𝑀𝐶𝑡𝑅𝐷 ∀𝑡 𝑅𝑈 𝑅𝐷  Remunerations: 𝑅𝑒𝑚𝑖,𝑡 , 𝑅𝑒𝑚𝑖,𝑡 ∀𝑖, 𝑡 𝑅𝑈 𝑅𝐷  Payments: 𝑃𝑎𝑦𝑗,𝑡 , 𝑃𝑎𝑦𝑗,𝑡 ∀𝑗, 𝑡

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Figure 2: Detailed diagram of the proposed RAS scheme

3.2. Stage 1: Day-ahead RAS bidding and DAUC

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3.2.1. RAS bidding for the next day A crucial characteristic of cost-based markets is that they are cleared based on the ISOaudited generation costs of each resource. As discussed earlier, we do not intend to change this paradigm for the energy market, but to explore an RAS mechanism to complement it so as to ensure an economically efficient and secure operation. Notice that we are generically referring to a mechanism of bids for the procurement of RAS. If deemed necessary, the ISO may impose limits on the bids so that they cover only the estimated costs incurred by the ramping resources, including costs associated to cycling of thermal units, increased maintenance due to greater wear and tear, opportunity costs of water in reservoirs, and additional fuel. However, estimating these types of costs by a centralized entity (e.g. the ISO) may be extremely impractical and cumbersome, as discussed in [25]. Otherwise, an hybrid market design may consider the use of economic bids for the procurement of ramping services and an audited-cost paradigm for the energy market. Nonetheless, a hybrid mechanism may also open the doors for strategic behavior and gaming in the market. The detailed analysis and comparison of both possibilities are outside the scope of our work, as the proposed mechanism is independent of the auction design. For each ramping product (either RAS up or RAS down ), each provider i will offer a bid-pair 7

{BidQi , BidPi } consisting on ramping capacity and its respective price (which could be an economic bid or an audited cost, depending on the design). The N bid-pairs will be ordered by the ISO from lower to higher cost. Thus, Ψ corresponds to the price-ordered set of bid pairs {BidQi , BidPi }, ∀i = 1, . . . , N.

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3.2.2. Estimation of RAS requirements for next day Preliminary RAS requirements for every 5 minute period t (ReqRU,pre and ReqRD,pre ) are est t timated before conducting the DAUC. As illustrated in [38], if the ramping requirements are too small, there may not be enough flexible resources committed, causing price spikes and reducing market efficiency. If the ramping requirement is too much, unnecessary capacity may be committed, inflating total start-up and min-run cost. Of course, day-ahead forecasts of ramping needs are not necessarily going to be accurate. In order to ensure that enough ramping capability is committed for the next day, it is preferable for the ISO to choose a conservative estimate. Furthermore, as discussed later, being conservative when estimating ramping requirements will raise marginal prices for the service, encouraging participation in the market. To forecast ramping needs for the following day in our case study, we conducted an analysis of the probability distribution of the actual mismatches between the predicted and the real net load for our test system. As in CAISO, the probability distribution function of the expected error of the net load forecast is approximated by a histogram constructed from historical observations in intervals representing conditions similar to the current ones [18, 37]. Of course, estimating ramping needs more than 24 hours in advance is more imprecise than doing it just a few minutes before, as VRE generation and demand is more uncertain. Thus, to account for the reduction of uncertainty of the shorter time horizon, 5 minutes before real operations the ramping requirements will be updated, as discussed in subsection 3.3.2.

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3.2.3. Day-Ahead Unit Commitment with RAS constraints DAUC is the process of deciding the resources to be committed in every interval of next day. Hence, UCi,t is a binary DAUC decision variable indicating the commitment status of resource i in interval t. As in any standard unit commitment formulation, GenUC i,t , i.e. the programmed dispatch or power injection to the system of resource i in interval t, will be limited by the disjunctive constraints in eq. (1). max UCi,t · Pmin ≤ GenUC i i,t ≤ UC i,t · Pi

(1)

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Notice that if UCi,t is 0, GenUC i,t will also be 0. Otherwise, the dispatch remains between its minimum and maximum limits Pmin and Pmax . Now, the programmed ramping capability of i i RU,pre RD,pre resource i in interval t, Capi,t and Capi,t , will be limited either by its spare capacity up (or down) or by its technical ramping limits, as per eq. (2): n CapRU,pre ≤ min UCi,t · Pmax − GenUC i i,t , i,t n min 0 ≤ CapRD,pre ≤ min GenUC i,t − UC i,t · Pi , i,t 0≤

o RUimax , ∀i, t o RDmax , ∀i, t i

(2)

The technical ramping limits (RUimax and RDmax ) reflect the fact that not all generators can i change their outputs at the same rate. Notice that eqs. (1) and (2) are technical and not economic constraints of the resources, in the sense that they do not consider the bidding process’ results. Constraints related to limits imposed by the bids are shown in eq. (3): 8

RU,pre Capi,t ≤

BidQRU · UCi,t , ∀i, t i

0 ≤ CapRD,pre ≤ i,t

BidQRD i · UC i,t , ∀i, t

0≤

(3)

Now, the key difference between current ISO’s DAUC processes and the integration of an RAS mechanism is given by the following equation: X CapRU,pre ≥ ReqRU,pre , ∀t t i,t ∀i

X

CapRD,pre ≥ ReqRD,pre , t i,t

(4) ∀t

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3.3. Stage 2: 5-minute ahead RAS procurement

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Including eq. (4) as a constraint in the DAUC ensures that in the next day there will be enough ramping resources available (and willing, since they presented RAS bids) to satisfy the preliminary ramping requirements set by the ISO in every interval of the planning horizon. Furthermore, inclusion of this constraint in the DAUC is an extra incentive for potential service providers to present RAS bids, as they otherwise might risk not being committed for the energy market during intervals with large ramping needs.

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3.3.1. Definition of supply functions for next interval The RAS supply functions (price versus quantity) are determined using the set of bid pairs Ψ obtained the previous day and the current system conditions, as not all RAS capacity will be available for every hour of the next day. For example, a generator may not be committed in a specific period, may be dispatched too close to its operational limits, or a bidder may suffer an outage impinging provision of the service. That is, the ramping capacity of bidder i actually RU available in interval t, CapRU i,t , will be less or equal than BidQi , as per eq. (3). Therefore, the RAS supply function for interval t is defined as follows: 0 ≤ X ≤ CapRU 1 Pk Pk RU RU i=1 Capi,t ≤ X < i=1 Capi,t , ∀k = 1, . . . , N PN RU X > i=1 Capi,t

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   BidPRU  1 ,    RU RU S upplyt (X) =  BidPk ,     VoRS RU ,

(5)

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The supply functions for the RAS down service are similar, but replacing RU by RD. PN If RAS needs X exceed the total capacity offered by the N bidders ( i=1 BidQRU i ), then the RAS will have a price equal to a predetermined Value of Ramping Shortage (VoRS ). Also, if ramping supply is insufficient, the ISO may impose a set of administrative measures, such as mandatory service’s provision or VRE curtailment, to secure the system’s reliability and economic efficiency. 3.3.2. Updating RAS requirements Ramping requirements for the next 5-minute interval t (either up ReqRU or down ReqRD t ) are t determined in period t − 1. Similar to [18, 37], they have components related to expected net load change and unexpected forecast error. The first component is the expected net load change ∆NDtprog between two consecutive intervals as a result of shifting electric demand and VRE generation, defined by: 9

∆NDtprog = NDtprog − NDreal t−1

(6)

ReqRD = t

n o max 0, ∆NDtprog + Requnc,up t o n − min 0, ∆NDtprog + Requnc,down t

≥0

(7)

≥0

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ReqRU = t

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Thus, in period t − 1, the ISO must estimate the future net demand in the next 5-minute interval (NDtprog ) based on some stochastic model of electric load and an estimation of future injections by VRE generators. Notice that ∆NDtprog can be positive or negative, depending on the change’s direction. The second set of variables (defined Requnc,up and Requnc,down ) covers net load forecast uncert t tainty. For our purposes we define these components for the next 5-minute interval using similar principles than CAISO’s FRP, although their calculation could be adapted to specific features of load and VRE in the system. Thusly, we are approximating the probability distribution function of the net load forecast error by a histogram constructed from historical observations obtained from execution of consecutive intervals representing conditions similar to the current ones, as in [18, 37]. In this manner, eq. (7) defines the ramping requirements as follows:

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Figure 3a illustrates how these different variables are related. Notice that, by definition, are both and ReqRD Requnc,up ≥ 0 and Requnc,down ≤ 0, but that the total requirements ReqRD t t t t defined positive. 𝑀𝑊

𝑀𝑊

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𝑅𝑒𝑞𝑡𝑅𝑈,𝑢𝑛𝑐

𝑹𝒆𝒒𝑹𝑼 𝒕

𝑝𝑟𝑜𝑔

𝑁𝐷𝑡

𝑝𝑟𝑜𝑔

𝑟𝑒𝑎𝑙 𝑁𝐷𝑡−1

∆𝑁𝐷𝑡

𝑅𝑒𝑞𝑡𝑅𝐷,𝑢𝑛𝑐

𝑺𝑹𝑫𝒕𝒓𝒆𝒂𝒍

𝑁𝐷𝑡𝑟𝑒𝑎𝑙

𝑟𝑒𝑎𝑙 𝑁𝐷𝑡−1

𝑺𝑹𝑼𝒓𝒆𝒂𝒍 𝒕 𝑹𝑼𝒓𝒆𝒂𝒍 𝒕

na

𝑹𝒆𝒒𝑹𝑫 𝒕

𝑡−1

𝑡

𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙

(a) Stages 1 & 2: Estimation of ramping requirements (adapted from [18, 37])

𝑡−1

𝑡

𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙

(b) Stage 3: Contributions to ramping requirement in real-time operation

Jo

ur

Figure 3: Definition of key variables related to ramping requirement in stages 1, 2, & 3

3.4. Stage 3: RAS provision in real-time operation

Load and VRE generation uncertainty dissipates in real-time operation. While one part of the RAS resources will cover the expected net load variation (NDtprog − NDreal t−1 ), the other part will satisfy ramping requirements due to the realization of net load’s uncertainty (NDreal − NDtprog ). t real Hence, the actual change in net load between periods t − 1 and t, ∆NDt , can be written as per eq. (8): 10

Expected net load change

∆NDreal t

Uncertainty realization

}| { z }| { z   prog real ND − ND + = NDtprog − NDreal t t t−1 = NDreal − NDreal t t−1  real   NDreal > NDreal RUt , t t−1 =  real −RDreal < NDreal t , NDt t−1

(8)

n o RUtreal = max 0, ∆NDreal t n o RDreal = max 0, −∆NDreal t t

≥0

ro of

Using an alternative notation, the actual ramping up or down (RUtreal and RDreal t , both defined positive) can also be calculated from eq. (9):

(9)

≥0

lP

re

-p

Therefore, RUtreal and RDreal represent the actual ramping need for period t. It follows that t only one of these variables will be different from zero at any given period, depending on the net load’s change direction. Ideally, the estimated requirements ReqRU and ReqRD defined in period t t real real t − 1 (stage 2) should be greater than RUt and RDt . Nevertheless, if the ramping requirement for period t was underestimated, RAS providers could still be called upon to provide the service, in the order defined by their bid prices. Notice that either RUtreal or RDreal will necessarily be t zero, as only one of the two services will be active in any given interval. Now, for payment and remuneration purposes, it is quite important to identify who is causing the ramping needs, and who among the RAS providers is actively changing its dispatch in order to satisfy them. Thus, to identify who is causing the net load change we can extend eq. (8) as follows: Load change

∆NDreal t

VRE generation change

z }| { zX }| { ∆GenVRE = ∆Loadt − j,t

(10)

na

∀ j∈GVRE

Jo

ur

where the first term represents the load change from period t − 1 to t, i.e. ∆Loadt = Loadt − Loadt−1 . The second term is driven by changing VRE generation patterns. Using a similar notation than in eq. (9), we can decompose the different contributions to the ramping up and down needs between periods t − 1 and t as follows: X n o n o S RUtreal = max 0, ∆Loadt + max 0, −∆GenVRE j,t S RDreal t

∀ j∈GVRE

X n o n o = max 0, −∆Loadt + max 0, ∆GenVRE j,t

(11)

∀ j∈GVRE

where S RUtreal

is the sum of all individual forces (load and VRE generation) causing ramping up needs, while S RDreal is the same but for RAS down . Of course, if in a period S RUtreal = t real S RDt , there won’t be any ramping up or down need. Hence, it follows that: ∆NDreal = S RUtreal − S RDreal t t 11

(12)

The relationship between these variables is illustrated in Fig. 3b. S RUtreal and S RDreal will t be used in stage 4 to allocate the payments to be made for RAS services. While changes in load and VRE generators are causing the ramping, RAS service providers i ∈ GRAS will need to change their energy injections to the grid to maintain the balance between generation and load. That is, the disbalance ∆NDreal will need to be matched by the change t in injections by the RAS service providers, as eq. (13) points out. The relationships between the RAS requirement and provision variables described by equations (8)-(13) will be further illustrated in Fig. 6. ∆NDreal = t

X

real Genreal i,t − Geni,t−1

=

ro of

∀i∈GRAS

X

X n o n o real real max 0, Genreal max 0, Genreal i,t − Geni,t−1 − i,t−1 − Geni,t {z } ∀i∈GRAS | {z } ∀i∈GRAS | real RUi,t ≥0

(13)

RDreal i,t ≥0

re

3.5. Stage 4: RAS pricing, remuneration, and payment

-p

real where RUi,t and RDreal i,t are, respectively, the ramping up and down actually performed by RAS resource i between intervals t − 1 and t (both variables are defined positive). Thus, total ramping is equal to the sum of individual changes in dispatch between consecutive intervals. Of course, RAS bidders will be called to provide the service sequentially from the lowest price to the highest, based on the ordered set of bids Ψ.

lP

While the estimation of RAS requirements and the mechanism of bids have many similarities to the ramping product in CAISO, the cost-based energy market existing in Chile (and many other countries) requires many adaptations to the pricing, remuneration, and payment schemes. This section discusses the necessary adaptations for the coexistence of a cost-based energy market and a bid-based RAS.

ur

na

3.5.1. RAS pricing Decoupling of energy and RAS prices is an important benefit of the proposed approach, as the larger baseload generation would be priced differently than energy supplied by more expensive resources to satisfy ramping needs, without overthrowing the merit order of generators [11], as was discussed in Section 1. Pricing for the provision of ramping services is determined using a pay-as-cleared paradigm, with a twist. The marginal cost for RAS up is calculated as:  n o RU RU real   if RU real > 0 max S upplyRU t (Reqt ), S upplyt (RU t ) RU MCt =  (14)  0 otherwise

Jo

The first term in (14) is calculated in t−1 from the supply function and the RAS up requirement imposed by the ISO (ReqRU t ). The second term is calculated in t from the supply function and the actual ramping need for the period (RUtreal ). The marginal cost for RAS down is calculated with a similar expression:  n o RD RD real   if RDreal > 0 max S upplyRD t (Reqt ), S upplyt (RDt ) RD MCt =  (15)  0 otherwise 12

If the RAS requirement was underestimated in period t−1, all providers will be paid at the bid price of the most expensive resource currently providing the service, following standard marginal pricing practices. If this were always the case, there might be insufficient incentives to participate in the RAS market by expensive potential providers, as they may only be able to recover their variable costs, without making a profit. This might not allow them to recover their fixed costs (the so-called ’missing money’ problem in cost-based markets [12]), potentially hindering the entrance of new participants to the market.

ro of

However, one should expect the ISO to not underestimate the requirement often, as this may jeopardize system security. If the RAS requirements were not underestimated, the valorization of RAS provision in the current period is at the system’s RAS marginal cost determined by RD S upplyRD t (Reqt ). That is, the ISO would compare the daily RAS supply curve (based on bids made in advance) and the RAS demand (requirement defined by the operator). This differentiates the proposed RAS from other ancillary services or a real time balancing market, as the price is being set based on a future ramping requirement determined by the operator in the previous 5minute period, and not by the services activated in the present. That is, the demand for the service (and the price) is set by the ISO’s perceived requirement of the system’s future ramping needs.

lP

re

-p

An important advantage of the proposed approach is that it ensures beforehand that the system will have sufficient ramping capacity for the next period (based on the ISO’s perceptions), and values the resources according to that. Also, if the RAS requirements were not underestimated, the most expensive RAS provider active in any given interval would still be making a profit (as it would be inframarginal), avoiding the need to create a separate capacity market to deal with the ’missing money’ problem.

3.5.2. Remuneration of RAS providers

na

For each interval t, the remuneration of RAS providers are calculated according to the actual mileage incurred by the generation units (or services) that provided ramping capability. That is, the compensation of RAS provider i is based on the actual change in power injection between real consecutive intervals so that it can provide the service: RUi,t and RDreal i,t ), as defined in eq. (13).

Jo

ur

Thus, remuneration for RAS only applies to those resources that changed their dispatch in the previous interval in a direction that compensated the ramping need. If a resource was not activated to provide ramping services, its remuneration for this concept would be 0. Notice that the proposed RAS for this type of market does not correspond to a reserve, as only the service’s activation will be remunerated, but not its availability. Also notice that only one of the ramping services (RAS up or RAS down ) will be activated in any given period. An important feature of the proposed scheme is that those providers not committed in the energy market but called to provide ramping services in period t will not define the energy price MCtenergy , but may define the RAS price (either MCtRU or MCtRU ). Another important feature is the inclusion of uplifts in the remuneration mechanism to ensure that RAS providers will not lose money in the energy market because of their participation in the RAS market. If RAS up was active in period t, then the total remuneration of provider i in the energy and RAS markets is defined by eq. (16) as follows: 13

RAS up remuneration, RemRU i,t

Energy remuneration

Remtotal i,t

}| { z }| { z n o energy energy real real RU ∆t · Gen · max 0, VC − MC = ∆t · Genreal · MC + + RU · MC i,t t t t i,t i,t i,t | {z } | {z } Cost-recovery uplift for supraRU marginal generators, U pli f ti,t

Remuneration from RAS market clearance

Energy remuneration

RAS down remuneration, RemRD i,t

z }| { z }| { n o energy prog energy real real RD = ∆t · Genreal · MC + ∆t · max 0, Gen − Gen + RD · MC · MC t t t i,t i,t i,t i,t | {z } | {z }

lP

Remtotal i,t

re

-p

ro of

(16) The energy injected to the grid (∆t·Genreal , where ∆t is the interval’s length) is paid separately i,t from the RAS service, and both products are priced differently. The extra energy injected (∆t · real RUi,t ) would be paid at the marginal cost of energy (thus paying for the extra variable fuel costs) real in the energy market, while the fact of changing the generation level RUi,t is paid in the RAS market (to cover their additional operating costs related to cycling). Thus, the first term is simply the remuneration for energy injected to the system in period t, valued at the system’s marginal cost of energy. Now, if due to the system’s ramping requirements a provider not originally scheduled in the energy market had to supply the need, it will be supramarginal. That is, its variable cost VCi,t will be greater than the system’s energy marginal cost MCtenergy . This may be, for example, the case of a fast peaking generator committed only to cover ramping requirements RU despite being outbidded in the energy market. In this case, an uplift payment (U pli f ti,t , second term in eq. (16)) is added so that it can recover its costs. The last term is its remuneration for participation in the RAS up market, valued at the cleared price MCtRU . The different components of RemRU i,t are illustrated in Fig. 4a. If RAS down was active in period t, then the total remuneration of provider i in the energy and RAS markets is defined by eq. (17) as follows:

Opportunity cost uplift for RD RD provision, U pli f ti,t

Remuneration from RAS market clearance

ur

na

(17) Again, the first term is simply the remuneration for energy injected to the system in period t. RD , Besides the remuneration for participation in the RAS down market, an uplift payment (U pli f ti,t second term in eq. (17)) is added so that provider i can recover the opportunity cost for supplying RAS down , hence reducing its generation, instead of receiving its income from the energy market. With this uplift payment we eliminate the risk associated to not knowing beforehand MCtenergy , which might be otherwise added with a premium to the RAS down bid prices or inhibit participation in this market. The different components of RemRD i,t are illustrated in Fig. 4b.

Jo

3.5.3. Payment of RAS services The total payments to be made for RAS are equal to the total remunerations to the providers, as per eq. (18). X RU PayRU RemRU (18) i,t T otal,t = RemT otal,t = ∀i

We propose a ’causer pays’ paradigm, in the sense that payments are distributed pro rata of their respective contribution to the ramping needs. Therefore, the total payment is divided 14

between ramping caused by load change from period t − 1 to t (i.e. ∆Loadt = Loadt − Loadt−1 ) and the one driven by changing VRE generation patterns. RD Now, RAS payments associated to load change, PayRU load,t and Payload,t , can be calculated pro rata of the load’s change contribution to the respective ramping need, as per eq. (19): n o max 0, ∆Loadt RU · PayRU Payload,t = T otal,t S RUtreal (19) n o max 0, −∆Loadt PayRD · PayRD load,t = T otal,t S RDreal t

re

-p

ro of

Notice that only one of the payments (RU or RD) will be active in any given period t, while the other will be zero. The payment corresponding to the load could be assigned to either all generators pro rata of their injections, or directly to the demand by some mechanism to be defined and outside the scope of this article. For our purposes, it is sufficient to choose the first option. RAS payments are similarly distributed among VRE generators causing the ramping need, pro rata of their respective change in generation (as long as its variation increments the ramping need active in the period), as shown in eq. (20): n o max 0, −∆GenVRE j,t RU · PayRU Pay j,t = T otal,t S RUtreal (20) n o max 0, ∆GenVRE j,t PayRD · PayRD j,t = T otal,t S RDreal t

ur

na

lP

VRE where ∆GenVRE = GenVRE j,t j,t − Gen j,t−1 corresponds to the ramping need caused by VRE generator j (up or down, depending on the sign). Notice that payment by VRE generator j occurs only if its generation change leads to a higher ramping need. It is also important to highlight that with this scheme VRE generators will find incentives to reduce their variations between periods, so that they are not held accountable for RAS payments. In this way, there will be an economic incentive to invest, for example, in local storage to smooth out their power injections. Finally, although other demand/supply mismatches caused by unexpected outages of generation or transmission infrastructure could also be included in the scheme, they are usually handled by other means, such as frequency control ancillary reserves. Thus, we consider them out of our scope.

4. Evaluation of proposed RAS scheme

Jo

4.1. Test system

Our model of the northern part of the NES consists of 117 buses, 162 transmission lines, and 82 generating power units (including different CCGT plant configurations). The year modeled is 2021, with a peak demand of 4321.71[MW] and assuming 19% VRE penetration. Net load variation for 2021 is depicted in Fig.5, with the contribution of VRE generation shown in green. Future expected ramping needs for a system this size are quite substantial, especially considering its predominantly large, aging, and inflexible thermal generation. The 15

𝑷𝒂𝒚𝑹𝑫 𝑻𝒐𝒕𝒂𝒍,𝒕

𝑹𝒆𝒎𝑹𝑫 𝑻𝒐𝒕𝒂𝒍,𝒕

𝑷𝒂𝒚𝑹𝑼 𝑻𝒐𝒕𝒂𝒍,𝒕

𝑹𝒆𝒎𝑹𝑼 𝑻𝒐𝒕𝒂𝒍,𝒕 ෍ 𝑼𝒑𝒍𝒊𝒇𝒕𝑹𝑼 𝒊,𝒕

෍ 𝑼𝒑𝒍𝒊𝒇𝒕𝑹𝑫 𝒊,𝒕

∀𝒊∈𝓡

∀𝒊∈𝓡

෍ 𝑷𝒂𝒚𝑹𝑫 𝒋,𝒕

෍ 𝑷𝒂𝒚𝑹𝑼 𝒋,𝒕

∀𝒋∈𝓖𝑽𝑹𝑬

∀𝒋∈𝓖𝑽𝑹𝑬 𝑹𝑼 ෍ 𝑹𝑼𝒓𝒆𝒂𝒍 𝒊,𝒕 ∙ 𝑴𝑪𝒕

𝑹𝑫 ෍ 𝑹𝑫𝒓𝒆𝒂𝒍 𝒊,𝒕 ∙ 𝑴𝑪𝒕

∀𝒊∈𝓡

∀𝒊∈𝓡

𝑷𝒂𝒚𝑹𝑫 𝒍𝒐𝒂𝒅,𝒕

Uplift for supra-marginal generators

RASup payment for VRE change

Uplift for opportunity cost of RASdown provision

Remuneration from RASup market clearance

RASup payment for load change

Remuneration from RASdown market clearance

RASdown payment for VRE change RASdown payment for load change

(b) RAS down in interval t2

-p

(a) RAS up in interval t1

ro of

𝑷𝒂𝒚𝑹𝑼 𝒍𝒐𝒂𝒅,𝒕

Figure 4: Diagram of RAS remuneration and payments in two different 5-min intervals

Jo

ur

na

lP

re

maximum up and down ramping needs are about 13.12[MW/min] and 12.15[MW/min], occurring in the evening and early in the morning, respectively. In the Atacama desert, where the test system is located, solar irradiance is variable but fairly predictable. Most ramping needs due to net load variation are caused by solar generation daily patterns, while ramping needs due to uncertainty are mostly caused by wind power unpredictability. Market simulations are conducted in PLEXOS (version 7.300 R02 x64) [39], a mixed integer programming based electricity and gas market simulation and optimization commercial platform. PLEXOS is suitable for both operational and planning simulation, and has been used in a number of renewable integration studies. PLEXOS allows to add customized generic decision variables and constraints to its formulation and the execution of user-defined code to modify its standard algorithms. Once the mathematical problem has been formulated, it is solved using the Xpress optimization solver [40]. As noted in Section 3.2.1, evaluation of bidding strategies for RAS is outside the scope of our work. Thus, instead of modeling strategic bidding behavior we estimate, for each resource, the costs it incurs for providing the service. Therefore, for evaluation purposes, in this paper the RAS offer prices will cover the estimated combustible variable costs plus cycling or unit wear costs of generating units providing the service, based on [41].

4.2. Cases evaluated The cases simulated in Section 5 pursue a dual purpose: (1) To evaluate the impact of reducing the time resolution from 1 hour to 5 minutes and the inclusion of ramping constraints in DAUC simulations, and (ii) To assess the market benefits of the proposed RAS scheme. Hence, the three cases examined can be described as follows: 16

5000

Demand Net Load VRE Generation

4500

3500 3000

12.15 [MW/min]

2500

13.12 [MW/min]

2000

03

0 h0

06

0 h0

09

0 h0

12

0 h0

15

0 h0

18

0 h0

21

0 h0

00

0 h0

-p

1500 0 h0 00

ro of

Power [MW]

4000

re

Figure 5: Typical net load profile in 2021 for the northern part of the NES

Case E1 (1hBASE). Base case with a 1-hour time resolution without ramping constraints. This case serves the purpose of representing the current operational paradigm for the NES.

lP

Case E2 (5mRR), considering a 5-minute time resolution with ramping constraints without the proposed RAS. The purpose of this case is showing the potential benefits of reducing DAUC time-resolution from 1 hour to 5 minutes and including ramping constraints in its formulation.

na

Case E3 (5mRR-RAS), considering a 5-minute time resolution with ramping constraints and the proposed RAS scheme. The objective of this case similar to case E2, but also showing the market benefits of the proposed RAS scheme.

ur

5. Simulation results

Jo

5.1. Technical evaluation

For each interval t, Fig. 6 shows the 5-minute-ahead ramping requirements (ReqRU and ReqRD ) and the actual RAS provision (RU real and RDreal ) in real time operation. The actual RAS provision follows the actual net load change ∆NDreal , caused by changes in demand (∆Load) and VRE generation (∆GenVRE ), as seen in equations (8) - (10). Similarly, the sum of changes causing ramping needs (S RU real and S RDreal ) are composed of ∆Load and ∆GenVRE , and the difference between S RU real and S RDreal corresponds to the real net load change ∆NDreal , as per equations (11)-(13). Notice that while some VRE generators go up in a time interval, some 17

others may be going down in the same interval. The ramping requirements and provision, either up or down, are always defined positive.

ro of

ReqtRU SRUtreal RUtreal ΔLoadtΔup) ΔGentVREΔup)

00 03h

00 06h

00 09h

00 12h

-p

ReqtRD SRDtreal RDtreal ΔLoadtΔdown) ΔGentVREΔdown)

00 15h

00 18h

re

Ramp Down [MW]

Ramp Up [MW]

100 80 60 40 20 0 100 80 60 40 20 0 00 00h

00 21h

00 00h

Figure 6: Ramping up/down requirement and total provision by RAS resources

Jo

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na

lP

For the day in which the simulations were conducted, most of the programmed net load variation ∆ND prog was caused by solar power daylight cycles (early morning and late evening), while net load uncertainty was mostly caused by wind generation during nighttime. In everyday practice, ramping requirements should not be underestimated. That is, RU real and RDreal should be less than ReqRU and ReqRD , respectively, for each interval t to avoid starting units out-of-merit. However, we intentionally underestimated the ramping requirement for a few hours in order to force the uplift remuneration mechanism to act and test its efficacy. Fig. 7 compares the ramping up and down actually required (and provided) for the three cases simulated, against the system’s net load. Case E1, with 1-hour time resolution and without ramping constraints, does not even see some of the variations occurring within each hour, and does not allocate the resources efficiently, increasing the system’s operational costs, as will be discussed in Section 5.2. For large amounts of VRE penetration, keeping with current practices may also force load shedding and VRE curtailment, or jeopardize the system’s security if using resources originally scheduled to provide operational reserves. In case E2, generation resources with ramping capabilities respond more erratically than in case E3, as some generators increase their injection at the same time than others decrease it, in an effort to reduce total system’s costs but disregarding the additional wear and tear caused by the extra cycling. That is, simply adding ramping constraints to unit commitment as in E2 reduces the total system cost with respect to using the proposed RAS scheme (see Table 1), but at the expense of potentially reducing the assets’ lifetime and increasing maintenance costs. With the proposed RAS scheme (case E3), we observe that the system always has enough resources to satisfy the ramping need, and that provision of the service is smoother than in case 18

E2, as illustrated in Fig. 8 for a gas-fired unit (U16).

4.0

100 75

3.5

25

3.0

0

2.5

ro of

−25 −50

0

0 00h

Net Load

00 00 00 00 00 00 00 00 03h 06h 09h 12h 15h 18h 21h 00h

-p

−100

2.0

Ramp Up , Ramp Down E1 Ramp Up , Ramp Down E2 real , RD real E3 RUtotal total, t ,t

−75

Net load [ GW]

Ramping [MW]

50

1.5

Figure 7: Comparison of up and down ramping between the three cases

re

60

−40

lP RUUreal 16, t

,

RDUreal 16, t

0

150 100

200

GenerationU16 E1 GenerationU16 E2 GenerationU16 E3

00 00 00 00 00 00 00 00 03h 06h 09h 12h 15h 18h 21h 00h

ur

0 0h0

na

0

[MW]

20

−20

250 Generation

Ramping [MW]

40

300

50 0

Jo

Figure 8: Generation profile for generator U16 and ramps awarded by the proposed RAS (case E3)

U16’s generation profile is on the primary axis, while its ramping up and down are on the secondary axis. Resource U16 obeys the signal from the RAS by increasing or decreasing its generation according to the ramping up or down requirements. Figure 9 shows the ramping provided for each interval, colored by RAS resource. We can observe that all the actual ramping need is covered by RAS resources that submitted bids to the market, so the proposed scheme allowed the ISO to commit enough resources to satisfy the expected ramping requirements. Also, 19

we observe that the cheapest RAS resources (usually combined-cycle LNG generators) are called first to provide the service, followed by coal and diesel units.

Ramp Up [MW]

100

RUtreal

LNG_TG2A LNG_U16 LNG_CTM3

HR_NORACID LNG_KELAR LNG_TG1A

80

COAL_CTM1 COAL_CTM2 COAL_U12

COAL_U13 COAL_U14 COAL_U15

COAL_ANG1 COAL_ANG2 COAL_CTH

COAL_U14 COAL_U15 COAL_ANG1

COAL_ANG2 COAL_CTH COAL_CTTAR

COAL_CTTAR COAL_CTA COAL_NTO1

COAL_NTO2 COAL_COCHI COAL_COCHII

60 40 20

RDtreal

HR_NORACID LNG_KELAR LNG_TG1A

80

LNG_TG2A LNG_U16 LNG_CTM3 COAL_CTM1

COAL_CTM2 COAL_U12 COAL_U13

60 40 20 00 03h

00 06h

00 09h

00 12h

COAL_CTA COAL_NTO1 COAL_NTO2

COAL_COCHI COAL_COCHII SING-SIC

00 15h

00 18h

00 21h

00 00h

re

0 00 00h

ro of

100

-p

Ramp Down [MW]

0

Figure 9: Ramping up/down provided by RAS resources in case E3

na

5.2. Economic evaluation

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Comparison between the cases suggests that implementing RAS optimizes the ramping of generating units and reduces their cycling, which translates into increased system security and a more sensible use of the generation assets.

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ur

Table 1 reports total and marginal system’s costs for all three cases, and Fig. 10 reports them by interval. Table 1 also compares the cost to load (CostT oLoad), representing the total cost of energy purchases plus the costs due to ramping caused by load variation. For the more general case of E3, and assuming that RAS payments associated to load change made by generators are passed through entirely to the demand (a common practice for any costs beyond energy in Chilean Power Purchase Agreements), we can express the cost to load as follows: X X X CostT oLoad = MCtenergy · Loadt + PayRU + PayRD (21) load,t load,t ∀t

|

∀t

{z Energy cost to clients

}

|

∀t

{z

}

Load RAS payments (only for case E3)

With the proposed RAS scheme the system’s energy marginal cost (MC energy ) decreases on average by 43.5% and 12.9% with respect to cases E1 and E2, respectively, due to decoupling of the energy and flexibility markets. That is, higher prices in cases E1 and E2 result from peaking generators setting the marginal price for all energy injections. Thus, an important feature of the proposed scheme is that energy and flexibility are priced differently. Figure 10 reports the RAS’ 20

Table 1: Total and marginal operating costs

Average MC Energy

Operation costs

Cost to load

[US D/MWh]

[MUS D]

[MUS D]

E1 (1hBASE)

59.17

2768.58

5209.66

E2 (5mRR)

38.37

2875.32

3904.21

E3 (5mRR-RAS)

33.43

2985.31

3831.52

Case

ro of

10 0 100

Op. Cost E1

Op. Cost E2

Op. Cost E3

MCtenergy E1

MCtenergy E2

MCtenergy E3

-p

50

50

0 00 00h

00

03h

00 06h

00 09h

MCtRU

re

0 100

00 12h

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Price [USD/MW]

Price [USD/MWh]

Cost [MUSD]

20

00

15h

00

18h

MCtRD

00

21h

00 00h

na

Figure 10: Comparison of marginal costs on the three cases

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marginal costs (MC RU and MC RD ). We observe that only one of the services is active (i.e. with price different than 0) at any given time. Meanwhile, total operation costs may apparently increase in case E3, but that ensues from including RAS constraints in the formulation and the internalization of ramping costs in the bids. We observe that the total cost to load is smaller in case E3 compared with the two other cases, despite the extra payment for RAS (60.18[MUS D]). This occurs because the largest component in eq. (21) is energy purchases (first term), and MC energy is lower in case E3. Thus, despite total operation costs with RAS increasing when compared to cases E1 and E2, marginal energy prices and final costs to clients remain lower. In other words, final users benefit as they pay a separate price for the energy they consume and for the ramping need they cause, instead of having to solely pay for energy at a higher price. In this way, generators whose inflexibility causes the commitment of more expensive resources in the first place are not rewarded for it. Also, the proposed RAS creates a separate market for ramping, improving economic efficiency and creating incentives for providing flexibility to system’s operations, potentially motivating 21

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the entry of emerging technologies such as fast generators, demand response, and utility-scale storage. The compensation scheme equitably distributed remuneration and payment obligations based on the origin of the ramping need and the contributing generation units, as shown in fig. 11. Units receiving the most compensation for RAS (either RU or RD) use liquified natural gas (LNG), followed by coal and diesel power plants. Likewise, the resources causing the variability (solar and wind) are the ones paying for RAS. The amount of money traded by concept of RAS reaches a daily total value of 101.1[MUS D] for RAS up and 72.1[MUS D] for RAS down , which only represents 4.6% of the 3771.3[MUS D] traded by the generation of energy. Notice that most RAS payments are due to VRE generation changes, as the mostly industrial load of the test system is relatively flat. Of course, the proportion of load and VRE payments may change for a system with other load profile, or for a different penetration of renewables.

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LNG_TG1A__ LNG_TG2A__ COAL_ANG1_ COAL_ANG2_ LNG_U16___ COAL_COCH2 COAL_COCH1 COAL_CTH__ COAL_CTA__ COAL_CTM2_ COAL_CTM1_ LNG_CTM3_ COAL_NTO2_ COAL_CTTAR COAL_NTO1__ LNG_KELAR_ COAL_U14__ COAL_U15__ CSP_CDOMIN COAL_U13__ COAL_U12__ WIND_CALAM PV_LAGUNAS PV_BOLERO_ WIND_VDLV_ PV_FINISTE PV_PARINAC HR_NORACID PV_CRUENC_ PV_CDOMINA PV_QUILLAG PV_HUATACO PV_BLUESKY PV_CALAMA_ HYDRO_CHAP PV_MARIAEL PV_CONDOR_ PV_URIBES_ PV_JAMA___ PV_LASCAR_ PV_SALIN__ PV_PALMONT PV_PULAR__ PV_ANDES__ PV_HUAYCA2 PV_ARICA2_ HYDRO_CAVA PV_SOLPARU PV_PAS3___ PV_LAHUAYC HYDRO_MHAH HYDRO_MHT2 PV_PAS2___ PV_ARICA1_ PV_PAMPAC1 PV_ELAGUIL PV_ARICA__

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Figure 11: Disaggregation of RAS total remunerations and payments, by resource

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Remuneration and payments, disaggregated by generator and interval, are shown in the heatmap in fig. 12. We observe that payments for solar PV plants are intensified during sunrise and sunset, which are also the periods in which the most flexible units (mostly LNG plants) get most of their remuneration. Wind farms payments are also intensified in the periods when they cause the most ramping needs. 6. Conclusion As stated in [12], “electricity markets must ensure reliability, deliver value for money, unleash technology and service innovation, and empower and protect consumers”. Regulators and market designers have long ago come up with solutions to procure, value, and remunerate electric energy and generation capacity following those precepts. However, recent challenges posed 22

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LNG_TG1A__ LNG_TG2A__ COAL_ANG1_ COAL_ANG2_ LNG_U16___ COAL_COCH2 COAL_COCH1 COAL_CTH__ COAL_CTA__ COAL_CTM2_ COAL_CTM1_ LNG_CTM3_ COAL_NTO2_ COAL_CTTAR COAL_NTO1__ LNG_KELAR_ COAL_U14__ COAL_U15__ CSP_CDOMIN COAL_U13__ COAL_U12__ WIND_CALAM PV_LAGUNAS PV_BOLERO_ WIND_VDLV_ PV_FINISTE PV_PARINAC HR_NORACID PV_CRUENC_ PV_CDOMINA PV_QUILLAG PV_HUATACO PV_BLUESKY PV_CALAMA_ HYDRO_CHAP PV_MARIAEL PV_CONDOR_ PV_URIBES_ PV_JAMA___ PV_LASCAR_ PV_SALIN__ PV_PALMONT PV_PULAR__ PV_ANDES__ PV_HUAYCA2 PV_ARICA2_ HYDRO_CAVA PV_SOLPARU PV_PAS3___ PV_LAHUAYC HYDRO_MHAH HYDRO_MHT2 PV_PAS2___ PV_ARICA1_ PV_PAMPAC1 PV_ELAGUIL PV_ARICA__

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Figure 12: RAS remunerations minus payments by resource, with 5-min resolution

by increasing penetration of VRE generation have also raised the need to create markets for operational flexibility.

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The proposed RAS is a relatively simple mechanism to adopt in cost-based markets as it can use already available capacity of generating units. Our proposal offers both technical and economic benefits, as it can procure ramping capability ahead of time while avoiding the use of more expensive peaking generators that would increase energy marginal prices. It also keeps the markets for energy and ramping needs separate, allowing for a more effective incentive allocation. Consequently, it can facilitate the integration of VRE resources and avoid their curtailment due to operational constraints of conventional generators.

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Implementation of the proposed RAS in practice would require additional research. One issue is the increased computational burden brought by the DAUC’s 5-minute resolution and the ramping constraints. In our computational experiments, the DAUC with 1-hour resolution took about a minute to run, while using 5-minute resolution plus ramping constraints took about 10 times more. As the DAUC does not need to run in real time, the increased simulation time was still reasonable for our purposes, but more research could be conducted to define a more efficient problem formulation. Furthermore, other practical issues still needing research relate to the design of an auction scheme, availability quantification tools, and performance verification metrics. 23

Acknowledgment This work was supported by Conicyt through projects Basal FB0008, Fondecyt 1151270 and the thesis program of Coordinador El´ectrico Nacional (Chile). References

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