Simulation of the operation of hydro plants in an electricity market using agent-based models
Summary (3 min read)
Introduction
- Finally, their reduced response times combined with storage capability turn hydro stations as an important technology to help the management of power systems having a large share of renewable genration associated to volatile primary resources as wind and solar.
- Agent-based simulation follows the metaphor of autonomous agents and multi-agent systems as the basis to conceptualize complex systems.
- After this introduction, Section II overviews the Iberian Electricity Market, given that the simulation of this market is the one of the goals of this research.
- Finally Section VI draws the most relevant conclusions.
A. New Structures and the Unbundling Model
- To allow an appropriate development of electricity markets, significant changes were needed in power systems.
- On the other hand, in order to nsure that the whole system operates properly, independent entities, (both at a technical and at a regulatory level) are required.
- In general, generation and retailing are provided un er competition while transmission and distribution grid activities are organized in regulated monopolies.
- In order to balance the demand and the supply new mechanisms have emerged, namely the day-ahead pool markets.
- The market clearing prices are typically obtained under a marginal basis nd are usually volatile, especially in countries where hydro and other renewable energies are present in large scale (as in MIBEL).
B. The Iberian Electricity Market, MIBEL
- Portugal and Spain power systems went through several changes in last decades.
- This organization started to change in 1995 when a new electricity law was passed admitting the coexistence of a public and a market driven sector.
- Later on, by the end of 1997 a new law was approved so that the Spanish electricity day-ahead market was in place in the 1st of January 1998.
- In terms of the renewable share, both countries were very successful in increasing the amount of renewables.
A. Hydro Scheduling Optimization
- Generation companies having hydro power plants in their portfolio have to identify the most adequate operation strategy in order to maximize their profit.
- In a competitive environment, they have to build selling bids (and buying when they have pumping) and send them to the day-ahead market operator.
- In addition to the uncertainty associated to the hydro conditions, the optimization of hydro power plants is a complex and nonlinear problem namely due to the nonli ear relation between the power, the flow and net head.
- [5] uses dynamic programming but this technique usually leads to the well-known “curse of dimensionality”.
- The mentioned nonlinear relation can also be addressed u ing an iterative procedure as described in [3].
B. Electricity Markets Modeling
- Optimization models typically address the maximization of the revenues of a single company, often considered as a price taker.
- Some examples were described in section III-A. Equilibrium Models represent the market behavior considering the competition between all participants.
- More recently, Agent-Based Models became an interesting alternative when the complex level prevents using tradi ional equilibrium framework.
- Agent-based computational economics (ACE) corresponds to the computational study of economic dynamic systems modelled as virtual worlds of interacting autonomous agents in an environment.
C. Agent-Based Models in Electricity Markets
- There are several models in the literature addressing this issue as AMES (Agent-based Modeling of Electricity Systems), EMCAS (Electricity Market Complex Adaptive Systems) and MASCEM (Multi Agent based Electricity Market).
- AMES is an open source platform that allows the simulation of strategic trading behaviors in restructured markets considering AC grids [10].
- EMCAS is linked to VALORAGUA model [11] that provides longer term operation planning strategies for hydro plants.
- Nevertheless, hydro generation, specially pumping hydro stations, is not adequately characterized taking into account the increase of renewable volatile sources.
- Taking this into account, the main objective of this research is to simulate hydro generation in a market environment using an ABM platform, especially regarding hydro with pumping given the extra flexibility these stations have in terms of buying electricity in off peak hours when eventually extra wind generation is available and selling it in peak hours.
A. Hydro Agents
- Hydro station agents bid their energy in the market and their strategy is very dependent on the type of reservoir and inflows.
- This parameter is given by a learning procedure modeled using a sigmoid function that reflects the risk profile of each agent.
- This strategy is an adaptation of the derivative-following strategy discussed in [13] and also used in [12].
- When an agent decides to pump, it has an expectation for the next day market prices.
C. Market and System Operator Agents
- The Market Operator agent is an artifact agent, given that it has not an associated decision making process [1].
- It performs the market clearing operations determining the market price and communicating the market results to all market agents.
- Regarding the System Operator, in this phase this agent is not used.
E. Regulater Agent
- This agent monitors the generator bids and can penaliz these agents if the bid prices are very different of the marginal cost regarding thermal stations or of the water value for hydro stations.
- The test case was based on a simplified version of the Portuguese generation system, to allow a better analysis of the results.
- The authors considered 22 hydro power plants having constant inflows and 11 thermal (coal and natural gas) units.
- Then the authors used the 2013 historical generation profile for these units.
- The demand is assumed totally inelastic and prepared to pay the maximum price admitted in MIBEL, 180 €/MWh.
A. Generation companies without strategies
- The first simulation assumes that generation companies bid without any special strategy, that is, their bids simply reflect the operation marginal cost of each station.
- O the other hand constant profiles were used for the demand and for the wind and PV units.
- Figure 3 shows the market price results if the generators bid their variable cost.
- The off peak prices are determined by run of river hydro and coal plants, while the peak prices result from bids from natural gas and reservoir plans.
- In this simulation the authors did not consider the possibility of pumping neither the monitoring action of the regulator.
B. Generation companies with strategies
- In the second simulation the authors considered that all generators have an higher risk profile which means that they will do aggressive bids up and down (for example all increasing or decreasing the bids by 1 €/MWh in each iteration).
- Given that the demand is completely inelastic, they rapidly realize that if they all bid the maximum price they will maximize their profits.
- If the bid prices is limited (blue line), the market prices are more stable.
- The results show that competition is now working and prices are very similar to the ones obtained considering the regulator limitation.
- For this reason, in their work the authors will consider that generators have different risk profiles and the regulator will not limit the bids, but instead it will check if the bids are excessively higher regarding the marginal cost.
C. Using the 2013 renewable and demand profiles
- The use of the real wind, PV and demand profiles for 2013 turns this simulation closer to reality.
- Initially the authors ran a simulation using these profiles, and then the number of natural gas plants was increased to foster competition.
- Figure 6 presents the results obtained for these two simulations.
- As expected the markets prices are higher when wind energy is more reduced.
- Table 1 has the annual average market prices for these simulations.
D. Comparison results without pumping hydros
- Finally, the authors analyzed a period having a large amount of wind energy with and without pumping stations.
- The results in Figure 7 show that if pumping is included, then zero price periods are more reduced suggesting that pumping hydro plants are able to do price arbitrage and to behave as price makers.
- This means they have to consider the difference between forecasted and real prices as a way to minimize their risk.
- Finally, the model will be completed including ancillary services markets namely for rese ves.
Did you find this useful? Give us your feedback
Citations
9 citations
Cites background from "Simulation of the operation of hydr..."
...which was discussed in [23] and [24] in the context of MIBEL....
[...]
...In [23], they found that accurate model for hydropower is important in Portuguese market as they often behave as price maker....
[...]
...[23] Design an electricity market considering hydropower stations Hydro, thermal, renewable gen, market, system operators, consumers, regulator Bidding strategy is function of water in reservoir, learning parameter of agent,decision support tool DA...
[...]
7 citations
Cites background from "Simulation of the operation of hydr..."
...Traditionally, deterministic hydro dispatch has been a subject of linear mixed-integer optimisation models such as [6] and has been integrated recently into an agent-based approach [7], which does...
[...]
4 citations
Cites background or methods from "Simulation of the operation of hydr..."
...As mentioned before the main goal of this paper is to introduce a Q learning procedure in the Agent-Based Hydro plants model detailed in [1], [2] and [3]....
[...]
...The developed ABM model considers four types of hydro agents having different bidding strategies [3] as briefly outlined below:...
[...]
...Depending on the type of hydro unit, the bidding price strategy is determined by the water value on the reservoir, by a learning parameter α and by a decision supporting tool, all of them originally described in [2, 3] and modeled by (2)....
[...]
...Hydro plants are modeled as agents that can produce and also consume (in the pumping case) meaning that they have to negotiate energy in the market as introduced in [2-3]....
[...]
...Accordingly, this paper presents a model using an agent-based environment that was originally described in [2-3] and in which we are now introducing a Q learning procedure....
[...]
4 citations
Cites methods from "Simulation of the operation of hydr..."
...In Sousa and Saraiva (2017), an ABM model is described using Q-learning to provide knowledge for the agents to select their decision....
[...]
1 citations
Cites background from "Simulation of the operation of hydr..."
...The introduction of competition in activities as generation and retailing justifies the development of new models and tools to ensure that the most adequate decisions are adopted [1, 2]....
[...]
References
195 citations
"Simulation of the operation of hydr..." refers background in this paper
...There are several works that were developed to model electricity markets using different techniques that can be organized in four main areas [9]:...
[...]
152 citations
152 citations
"Simulation of the operation of hydr..." refers methods in this paper
...Th is strategy is an adaptation of the derivative-following strategy discussed in [13] and also used in [12]....
[...]
...Finally, the MASCEM is a simulation platform based on a multi-agent framework [12]....
[...]
101 citations
88 citations
"Simulation of the operation of hydr..." refers methods in this paper
...Th is strategy is an adaptation of the derivative-following strategy discussed in [13] and also used in [12]....
[...]
Related Papers (5)
Frequently Asked Questions (16)
Q2. What is the purpose of the restructuring of the electricity sector?
The electricity sector restructuring originated the unbundling of the traditional vertically integrated companies and the creation of a disaggregated structure involving activities covering the entire value chain, namely generation, transmission, distribution and retailing.
Q3. How many MW of wind power were installed in Portugal?
By the end of 2014, wind power reached an installed capacity of 5270 MW out of 17827 MW in Portugal (30%) and of 22854 MW out of 102259 MW in Spain (22 %) with a contribution to supply the demand of 25% in Portugal and 21% in Spain.
Q4. What is the role of the Regulatory Agency?
The Regulatory Agency was created in 1995 and is responsible for the publication of several codes and for setting the regulated tariffs.
Q5. What are the types of agents that can be used to model the behaviour of consumers?
These include two types of agents: inelastic agents that buy energy at the maximum value allowed in the MIBEL rules (180 €/MWh), and elastic agents that are designed to model the behaviour of consumers that can directly participate in the market, typically large industries or hydro pumping stations.
Q6. What is the definition of a competitive environment?
In a competitive environment, they have to build selling bids (and buying when they have pumping) and send them to the day-ahead market operator.
Q7. What is the possibility of imposing a limit to the bidding prices?
It also has the possibility of imposing a limit to the bidding prices so that they adhere more closely to the marginal cost of thermal stations or to the water value of hydro stations.
Q8. What are the day-ahead markets in Portugal?
The day-ahead markets that exist in several European countries correspond to short term forward markets based on the matching of the selling and buying bids for each hour of the next day.
Q9. What was the first time the electricity prices in Portugal and Spain were different?
In the first operation years the electricity prices in the two areaswere different in a large number of hours due to the application of market splitting to solve congestion in the interconnections.
Q10. What is the first simulation of the Portuguese generation system?
The first simulation assumes that generation companies bid without any special strategy, that is, their bids simply reflect the operation marginal cost of each station.
Q11. What is the maximum price the demand is prepared to pay?
Although there is no communication between generators, after same time all of them start biding close to 180 €/MWh, which is the maximum price the demand is prepared to pay.
Q12. What are some examples of equilibrium models?
Some examples were described in section III-A. Equilibrium Models represent the market behavior considering the competition between all participants.
Q13. What is the role of the regulators in the electricity market?
On the other hand, in order to ensure that the whole system operates properly, independent entities, (both at a technical and at a regulatory level) are required.
Q14. What is the impact of more competition on peak periods?
In blue the authors can also observe the impact of having more competition on peak periods due to more natural gas power plants and leading to peak price reductions.
Q15. What are the actors involved in the generation and retailing activities?
It also includes multiple actors as regulatory agencies, market and system operators, and several agents in the generation and retailing activities that are provided under competition.
Q16. What is the bidding price strategy for hydro station agents?
Depending on the hydro type, the bidding price strategy is determined by the water value on the reservoir, by a learning parameter α and by a decision supporting tool, all of them originally described in [2] and modeled as (1).