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Previously works on analysing imperfect electricity markets have employed conventional game-theoretic approaches. However, such approaches necessitate that each strategic market player has full knowledge of the operating parameters and the strategies of its rivals as well as the computational algorithm of the market clearing process. This unrealistic assumption, along with the modeling and computational complexities, renders such approaches less applicable for conducting practical multi-period and multi-spatial equilibrium analysis. This paper proposes a novel multi-agent deep reinforcement learning (MA-DRL) based methodology, combining multi-agent intelligence, the deep policy gradient (DPG) method, and an innovative long short term memory (LSTM) based representation network for optimizing the offering strategies of multiple self-interested generation companies (GENCOs) as well as exploring the market outcome stemming from their interactions. The proposed approach is tailored to align with the nature of the examined problem by posing it, for the first time, in multi-dimensional continuous state and action spaces, enabling GENCOs to receive accurate feedback regarding the impact of their offering strategies on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, and thereby facilitates more accurate equilibrium analysis. The proposed LSTM-based representation network extracts discriminative features which further improves the learning performance and thus promises more profitable offerings strategies for each GENCO. Case studies demonstrate that the proposed method i) achieves a significantly higher profit than state-of-the-art RL methods for a single GENCO's optimal offering strategy problem and ii) outperforms the state-of-the-art equilibrium programming models in efficiently identifying an imperfect market equilibrium with/without network congestion. Quantitative economic analysis is carried out on the obtained equilibrium.

Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants’ physical non-convex operating characteristics, while conventional RL methods require discretization of state and / or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multidimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favorable computational performance than benchmark RL methods due to the employment of the PER strategy.

Distributed, consensus-based algorithms constitute a promising approach for the coordination of distributed energy resources (DER) due to their practical advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the coordination problem in independent time periods and therefore are inherently unable to capture the time-shifting flexibility of the demand side. This paper demonstrates that state-of-the-art algorithms fail to converge when time-shiftable flexible demands (TSFD) are present. In order to address this fundamental limitation, a relative maximum power restriction is introduced, which effectively mitigates the concentration of the TSFD responses at the same time periods and steers the consensus-based algorithm towards a feasible and near-optimal solution.

Previous work has analyzed the renewable generation hosting capacity of electricity systems and the relevant value of flexible demand from a techno-economic perspective, considering the balancing and network challenges associated with a large-scale integration of renewables. This paper investigates these issues from a market perspective, considering the challenges of low energy prices and renewables’ investment cost recovery. In this context, a multi-period bi-level optimization model is developed, where the upper level maximizes the renewable generation capacity subject to the long-term profitability constraint and the lower level represents the market clearing process, accounting for the time-coupling operational constraints of flexible demand. This bi-level problem is solved after converting it to a single-level mixed-integer linear problem (MILP). Case studies validate the proposed model and demonstrate that demand flexibility increases the maximum renewable generation capacity that can be integrated in the system without violating its profitability constraint.

Previous work has analyzed the role of energy storage (ES) on generation investment planning through centralised cost-minimization models which are inherited from the era of regulated electricity utilities. This paper investigates this issue in the context of the deregulated market environment by proposing a new strategic generation investment planning model. The decision making of a strategic generation company is modeled through a multi-period bi-level optimization problem, where the upper level determines the profit-maximizing investment decisions of the generation company and the lower level represents the market clearing process, accounting for the time-coupling operational characteristics of ES. This bi-level problem is solved after converting it to a single-level mixed-integer linear problem (MILP). Case studies demonstrate that the introduction of ES reduces the total generation capacity investment and enhances investments in “must-run” base-load generation over flexible peaking generation, yielding significant system cost savings.

Previous work has demonstrated the significant benefits of demand flexibility for low-carbon electricity systems. However, given the deregulation of the electricity industry, this flexibility also affects the business case of retailers, an aspect which has not yet been thoroughly investigated. This paper proposes a new multi-period bi-level optimization model capturing the interactions between a retailer's offered tariffs to the served consumers, the consumers' flexibility and the wholesale market in an integrated fashion. This bi-level problem is efficiently solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC), and subsequently to a Mixed-Integer Linear Problem (MILP). Case studies demonstrate that the time-shifting flexibility of the consumers reduces the retailer's revenue, since consumers can respond more effectively to strategic retail pricing patterns, but also reduces the retailer's cost, since it leads to flatter wholesale demand and price profiles, an effect which is not captured by previous work. Finally, the results indicate that the impact of this flexibility on the retailer's business case depends on the level of competition with other retailers in the market.

The envisaged decarbonization of electricity systems has attracted significant inte rest around the role and value of demand flexibility . However, the impact of this flexibility on generation investments in the deregulated electricity industry setting remains a largely unexplored area, since previous relevant work neglects the time - coupli ng nature of demand shifting potentials. This paper addresses this challenge by proposing a strategic generation investment planning model expressing the decision making process of a self - interested generation company and accounting for the time - coupling o perational characteristics of demand flexibility. This model is formulated as a multi - period bi - level optimization problem, which is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC). Case studies with the proposed mo del demonstrate that demand flexibility reduces the total generation capacity investment, enhances investments in baseload generation and yield s significant economic benefits in terms of total system costs and demand payments.

Previous work has demonstrated that the price elasticity of the demand side reduces electricity producers' ability to exercise market power. However, price elasticity cannot capture alone consumers' flexibility, as the latter mainly involves shifting of loads' operation in time. This paper provides for the first time qualitative and quantitative analysis of the value of demand shifting in mitigating market power by the generation side. An equilibrium programming model of the oligopolistic market setting is developed, taking into account the inter-temporal characteristics of demand shifting. The decision making process of each strategic producer is modelled through a bi-level optimization problem, which is solved after transforming it to a Mathematical Program with Equilibrium Constraints (MPEC). The market equilibria resulting from the interaction of multiple independent producers are determined by employing an iterative diagonalization method. Case studies on a test market with day-ahead horizon and hourly resolution quantitatively demonstrate the benefits of demand shifting in limiting generation market power, by employing relevant indexes from the literature.

Although previous work has demonstrated the ability of large energy storage (ES) units to exercise market power by withholding their capacity, it has adopted modeling approaches exhibiting certain limitations and has not analyzed the dependency of the extent of exercised market power on ES operating properties. In this paper, the decision making process of strategic ES is modeled through a bi-level optimization problem; the upper level determines the optimal extent of capacity withholding at different time periods, maximizing the ES profit, while the lower level represents endogenously the market clearing process. This problem is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. Case studies on a test market quantitatively analyze the extent of capacity withholding and its impact on ES profit and social welfare for different scenarios regarding the power and energy capacity of ES.

Uniform marginal pricing cannot generally support competitive equilibrium solutions in markets with non-convexities, yielding surplus sub-optimality effects. Previous work has identified non-convexities associated with the generation side of electricity markets and proposed different approaches to address surplus sub-optimality. This paper extends this concept to incorporate the demand side. Non-convexities of flexible demand (FD) are identified, including options to forgo demand activities as well as discrete and minimum power levels, and resulting surplus sub-optimality effects are demonstrated through simple examples and a larger case study. Generalized uplift and convex hull pricing approaches addressing these effects are extended to account for FD non-convexities. Concerning the former, generalized uplift functions for FD participants are proposed, and a new rule is introduced for equitable distribution of the total surplus loss compensation among market participants. Regarding the latter, it is demonstrated that convex hull prices are flattened at periods when FD is scheduled to eliminate surplus sub-optimality associated with the FD ability to redistribute energy requirements across time.

Although recent studies have investigated the impacts of energy storage on various aspects of power system operation and planning, its role in imperfect electricity markets has not been explored yet. This paper provides for the first time theoretical and quantitative evidence of the beneficial impact of energy storage in limiting market power by generation companies. Quantitative analysis is supported by a bi-level optimization model of the imperfect electricity market setting, accounting for the time-coupling operational constraints of energy storage. This bi-level problem is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC). Case studies are carried out on a test market with day-ahead horizon and hourly resolution.

Uniform marginal prices cannot generally support competitive equilibrium solutions in markets with non-convexities and yield schedules' inconsistency and surplus sub-optimality effects. Previous work has identified non-convexities associated with the generation side of electricity markets and proposed a generalized uplift approach to eliminate these effects. This paper examines the above issues from the perspective of the flexible demand (FD) side. FD non-convexities are identified, including its ability to forgo demand activities and minimum power levels, and resulting inconsistency and surplus sub-optimality effects are demonstrated through simple examples. Generalized uplift functions for FD participants are proposed, including quadratic pricing terms to limit their tendency to concentrate at the lowest-priced periods, and binary terms associated with their ability to forgo activities. Finally, a new rule is proposed for the equitable distribution of the total surplus loss among the market participants. These contributions are supported by case studies on a market with a day-ahead horizon and hourly resolution.

This paper tests the relationships between competencies of new venture teams (NVTs) and venture performances in high-tech firms are different from those of low-tech fields. Results show that there is no significant relationship between competencies and performances in low-tech sample. Contrarily in high-tech sample: 1) NVT's leader's strategy competency relates to business growth and relationship competency relates to invest efficiency and business growth; 2) Member level's member individual competency, member complementary competency and balanced role distribution all relate to all venture performances; 3) Team level's team knowledge, team skill and team attitude all are related to business growth.

This paper develops a conceptual model including three level competencies of entrepreneurial team and environment to comprehensively understand ET's new venture performance. We specify competencies of each level, including leader's individual-oriented and team-oriented Competencies; member's individual competencies, member complementary competencies and balanced role distribution; team level's knowledge, skill and attitude. This paper also advances a series of corresponded propositions according to the model.

Most international companies utilize organizational websites as a key component of strategic human resource management for attracting applicants. This article reviews the existing literature to understand the factors influencing organizational attractiveness. Drawing from Signaling theory and Person-Organization fit, this article proposes that organizational social image, organizational work image, and organizational website image are signals from organization, called organizational signaling. This paper also proposes a framework to explain how and why the three images and individual differences influence online organization attraction.

Bi-level optimization constitutes the most common mathematical methodology for modeling the decision-making process of strategic generation companies in deregulated electricity markets. However, previous models neglect the physical non-convex operating characteristics of generation units, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in the context of markets with complex biding and unit commitment clearing mechanisms. Aiming at addressing this fundamental limitation, this paper delves into deep reinforcement learning-based approaches by proposing a novel deep Q network (DQN) method and enabling explicit incorporation of these non-convexities into the bi-level optimization model. Case studies demonstrate the superior performance of the proposed method over the conventional Q-learning method in devising more profitable offering strategies.