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Controllable vs. Random: Renewable Generation Competition in a Local Energy Market
... The distribution of wind or solar power can be characterized using the historical data, which is known to the renewable energy suppliers. 1 As renewables usually have extremely low marginal production costs compared with traditional generators, we assume zero marginal production costs for the suppliers [18] [19]. ...
... We propose a probability-based method using historical data of renewable generations to compute the storage capacity. Note that suppliers charge and discharge the storage to maintain his output at the mean value of the random renewable generations as shown in (1). 16 Therefore, the charge and discharge amounts are also random variables, and we characterize the storage capacity such that its energy level will not exceed the storage capacity with a targeted probability. ...
... To begin with, we set a probability target α, and we aim to find a storage capacity S i such that the energy level in the storage exceeds the capacity with a probability no greater than α. Specifically, the with-storage supplier i will charge and discharge storage with value CD m,t i at hour t of month m as shown in (1). We assume that the initial energy level of storage is fixed for all the months and denote it as S l i . ...
Renewable energy generations and energy storage are playing increasingly important roles in serving consumers in power systems. This paper studies the market competition between renewable energy suppliers with or without energy storage in a local energy market. The storage investment brings the benefits of stabilizing renewable energy suppliers’ outputs, but it also leads to substantial investment costs as well as some surprising changes in the market outcome. To study the equilibrium decisions of storage investment in the renewable energy suppliers’ competition, we model the interactions between suppliers and consumers using a three-stage game-theoretic model. In Stage I, at the beginning of the investment horizon (containing many days), suppliers decide whether to invest in storage. Once such decisions have been made (once), in the day-ahead market of each day, suppliers decide on their bidding prices and quantities in Stage II, based on which consumers decide the electricity quantity purchased from each supplier in Stage III. In the real-time market, a supplier is penalized if his actual generation falls short of his commitment. We characterize a price-quantity competition equilibrium of Stage II in the local energy market, and we further characterize a storage-investment equilibrium in Stage I incorporating electricity-selling revenue and storage cost. Counter-intuitively, we show that the uncertainty of renewable energy without storage investment can lead to higher supplier profits compared with the stable generations with storage investment due to the reduced market competition under random energy generation. Simulations further illustrate results due to the market competition. For example, a higher penalty for not meeting the commitment, a higher storage cost, or a lower consumer demand can sometimes increase a supplier’s profit. We also show that although storage investment can increase a supplier ’s profit, the first-mover supplier who invests in storage may benefit less than the free-rider competitor who chooses not to invest.
... The distribution of wind or solar power can be characterized using the historical data, which is known to the renewable energy suppliers. 1 As renewables usually have extremely low marginal production costs compared with traditional generators, we assume zero marginal production costs for the suppliers [18] [19]. ...
... 2 Since the electricity demand is usually inelastic [5], we also assume the following. 1 In Section VIII of simulations, we use historical data to model the empirical CDF of renewable generations, which is explained in detail in Appendix.XIV. 2 The day-ahead prediction of consumers' aggregated demand can be fairly accurate [24]. We assume that the demand and supply mismatch due to the demand forecast error will be regulated by the operator in the real-time market. ...
... We verify that in both cases (1) and (2), min(D, y * −i (l, ϕ −i )) = y * −i (l, ϕ −i ). According to Lemma 1, the equilibrium revenue of both suppliers will be 22 Note that π RE −i (ϕ) > 0 since the lower support l > 0. ...
This paper studies the duopoly competition between renewable energy suppliers with or without energy storage in a local energy market. The storage investment brings the benefits of stabilizing renewable energy suppliers' outputs, but it also leads to substantial investment costs as well as some surprising changes in the market outcome. To study the equilibrium decisions of storage investment in the renewable energy suppliers' competition, we model the interactions between suppliers and consumers using a three-stage game-theoretic model. In Stage I, at the beginning of the investment horizon, suppliers decide whether to invest in storage. Once such decisions have been made, in the day-ahead market of each day, suppliers decide on their bidding prices and quantities in Stage II, based on which consumers decide the electricity quantity purchased from each supplier in Stage III. In the real-time market, a supplier is penalized if his actual generation falls short of his commitment. We characterize a price-quantity competition equilibrium of Stage II, and we further characterize a storage-investment equilibrium in Stage I incorporating electricity-selling revenue and storage cost. Counter-intuitively, we show that the uncertainty of renewable energy without storage investment can lead to higher supplier profits compared with the stable generations with storage investment due to the reduced market competition under random energy generation. Simulations further illustrate results due to the market competition. For example, a higher penalty for not meeting the commitment, a higher storage cost, or a lower consumer demand can sometimes increase a supplier's profit. We also show that although storage investment can increase a supplier 's profit, the first-mover supplier who invests in storage may benefit less than the free-rider competitor who chooses not to invest.
In this paper, we endeavor to address the problem of dynamic energy scheduling scheme for end-users with storage devices in smart grid. An end-user with an energy storage device is developed, which draws energy from multiple energy sources: local energy suppliers and external power grid. Our goal is to minimize the end-user’s total costs (including energy usage cost and storage cost) over long-term time under uncertainty (time-varying electricity price, uncertain energy output and demand). Concerning the energy storage cost, the resulting problem is modeled as a stochastic optimization case. Following this trend, based on the improved Lyapunov optimization, a dynamic energy scheduling algorithm with lower complexity is developed. It is theoretically proved that the proposed algorithm can make the optimization target asymptotically close to optimum. Simulation results are given to demonstrate that proposed algorithm can significantly save total costs than existing schemes where energy scheduling without the energy storage cost constraint.
Renewable resources are starting to constitute a growing portion of the total generation mix of the power system. A key difference between renewables and traditional generators is that many renewable resources are managed by individuals, especially in the distribution system. In this paper, we study the capacity investment and pricing problem, where multiple renewable producers compete in a decentralized market. It is known that most deterministic capacity games tend to result in very inefficient equilibria, even when there are a large number of similar players. In contrast, we show that due to the inherent randomness of renewable resources, the equilibria in our capacity game becomes efficient as the number of players grows and coincides with the centralized decision from the social planner's problem. This result provides a new perspective on how to look at the positive influence of randomness in a game framework as well as its contribution to resource planning, scheduling, and bidding. We validate our results by simulation studies using real world data.
We investigate group formations and strategic behaviors of renewable power
producers in electricity markets. These producers currently bid into the
day-ahead market in a conservative fashion because of the real-time risk
associated with not meeting their bid amount. It has been suggested in the
literature that producers would bid less conservatively if they can form large
groups to take advantages of spatial diversity to reduce the uncertainty in
their aggregate output. We show that large groups of renewable producers would
act strategically to lower the aggregate output because of market power. To
maximize renewable power production, we characterize the trade-off between
market power and generation uncertainty as a function of the size of the
groups. We show there is a sweet spot in the sense that there exists groups
that are large enough to achieve the uncertainty reduction of the grand
coalition, but are small enough such that they have no significant market
power.We consider both independent and correlated forecast errors under a fixed
real-time penalty. We also consider a real-time market where both selling and
buying of energy are allowed. We validate our claims using PJM and NREL data.
Wind energy is a rapidly growing source of re-newable energy generation. However, the current extra-market approach to its assimilation into the electric grid will not scale at deep penetration levels. In this paper, we investigate how an independent wind power producer might optimally offer it's variable power into a competitive electricity market for energy. Starting with a stochastic model for wind power production and a model for a perfectly competitive two-settlement market, we derive explicit formulae for optimal contract offerings and the corresponding optimal expected profit. As wind is an inherently variable source of energy, we explore the sensitivity of optimal expected profit to uncertainty in the underlying wind process. We also examine the role of forecast information and recourse markets in this setting. We quantify the role of reserves in increasing reliability of offered contracts and obtain analytical expressions for marginal profits resulting from investments in improved forecasting and local auxiliary generation. The formu-lae make explicit the relationship between price signals and the value of such firming strategies.
This paper studies the market power of generators in the electricity market when transmission capacity is scarce. We consider a simple world of two generators providing electricity to their consumers through a single transmission line. In the literature, different Cournot equilibrium concepts have been developed. This paper applies these concepts and explains the implicit assumptions on the behavior of the System Operator made in those papers. We show that these implicit assumptions are not realistic. For an alternative role of the System Operator, we solve the Cournot equilibrium and compare the outcome. Furthermore, we show that the axiomatic equilibrium concept of Smeers and Wei (1997) is linked with the model of Oren (1997) and can also be defined as a Nash Equilibrium.
Renewable resources are starting to constitute a growing portion of the total generation mix of the power system. A key difference between renewables and traditional generators is that many renewable resources are managed by individuals, especially in the distribution system. In this paper, we study the capacity investment and pricing problem, where multiple renewable producers compete in a decentralized market. It is known that most deterministic capacity games tend to result in very inefficient equilibria, even when there are a large number of similar players. In contrast, we show that due to the inherent randomness of renewable resources, the equilibria in our capacity game becomes efficient as the number of players grows and coincides with the centralized decision from the social planner's problem. This result provides a new perspective on how to look at the positive influence of randomness in a game framework as well as its contribution to resource planning, scheduling, and bidding. We validate our results by simulation studies using real world data.
The overall goal of the electricity market is to provide electricity in an efficient way while meeting the demands of the consumers. The competition and regulations are the different way to achieve this goal. The traditional electricity market is facing challenges in integrating the new sources of generation of electricity, technology, infrastructure, increasing demand and the consumer-oriented market. It has shifted the paradigm towards new market design which will be able to fit in existing market structure. One of such electricity market is local electricity market. The benefits of local power generation, storage and demand response all cooperate together and peer-to-peer model enables the participation of all participants of the market. It uses information and communication technology (ICT) for sustainable and efficiently transfer of electricity to the consumers. It integrates the distributed generation, microgrid, and smart grid into one electricity market at distribution side.
Renewable Power Generation systems are currently preferred for clean power generation. However due to their intermittent and unpredictable nature, energy storage needs to be used to ensure that the load is met at all times. There are many possible options for energy storage and the most popular and technologically matured option, batteries, is the subject of this paper. This paper explores the importance and necessity of batteries within isolated Renewable Energy systems, specifically hybrid PV and wind power generation systems. The paper looks at different varieties of batteries with a specific emphasis on lead-acid batteries. To integrate batteries into renewable energy system models, the system and the energy storage, must be simulated to test the impact on as well as optimise the sizing of the system in terms of cost and efficiency. There are currently many methods of modelling batteries. These and various additional methods of simulation and optimisation of storage and battery components are discussed in this paper.
Half-Title PageTitle PageCopyright PageDedication PageTable of ContentsPreface
This paper studies Cournot competition with two generators who share one transmission line with a limited capacityto supply price-taking consumers. In such a game the network operator needs a rule to allocate transmission capacity. Three rules are studied: all-or-nothing, proportional, and efficient rationing. The first result is that if the network operator taxes the whole congestion rent, the generators strategically change their production quantities, such that the network operator obtains no congestion rent. This gives poor incentives for investment in transmission capacity. The second result is that the network operator can create competition among the generators, which can increase welfare. Marginal nodal congestion pricing, which is optimal under perfect competition, is sub-optimal when generators can set their production quantities freely. It does not generate revenue for the network operator, nor does it increase competition among the generators.
We study the efficiency of oligopoly equilibria in a model where firms compete over capacities and prices. Our model economy corresponds to a two-stage game. First, firms choose their capacity levels. Second, after the capacity levels are observed, they set prices. Given the capacities and prices, consumers allocate their demands across the firms. We establish the existence of pure strategy oligopoly equilibria and characterize the set of equilibria. We then investigate the efficiency properties of these equilibria, where “efficiency” is defined as the ratio of surplus in equilibrium relative to the first best. We show that efficiency in the worst oligopoly equilibria can be arbitrarily low. However, if the best oligopoly equilibrium is selected (among multiple equilibria), the worst-case efficiency loss is with N firms, and this bound is tight. We also suggest a simple way of implementing the best oligopoly equilibrium.
Evolution of the Distribution System & the Potential for Distribution-level Markets: A Primer for State Utility Regulators
- S Thomas
S. Thomas, Evolution of the Distribution System & the Potential for
Distribution-level Markets: A Primer for State Utility Regulators. NA-RUC Research Lab, January 2018.