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The physical nature of electricity generation and delivery creates special problems for the design of efficient markets, notably the need to manage delivery in real time and the volatile congestion and associated costs that result. Proposals for the operation of the deregulated electricity industry tend towards one of two paradigms: centralized and decentralized. Transmission congestion management can be implemented in the more centralized point‐to‐point approach, as in New York state, where derivative transmission congestion contracts (TCCs) are traded, or in the more decentralized flowgate‐based approach. While it is widely accepted that theoretically TCCs have attractive properties as hedging instruments against congestion cost uncertainty, whether efficient markets for them can be established in practice has been questioned. Based on an empirical analysis of publicly available data from years 2000 and 2001, it appears that New York TCCs provided market participants with a potentially effective hedge against volatile congestion rents. However, the prices paid for TCCs systematically diverged from the resulting congestion rents for distant locations and at high prices. The price paid for the hedge not being in line with the congestion rents, i.e., unreasonably high risk premiums are being paid, suggests an inefficient market. The low liquidity of TCC markets and the deviation of TCC feasibility requirements from actual energy flows are possible explanations.

The non‐storable nature of electricity and the increasing complexity of financial instruments as a tool for hedging against risk make the area of research very useful in the real world. Many power portfolio optimization problems have been developed to combat the issue of risk tolerance, but very few (if any) have included transmission constraints. The purpose of this paper is to consider optimization of portfolios of real and contractual assets, including derivative instruments, in a multi‐period setting where transmission constraints also exist.

Rather than using a flowgate constraint as a representation of transmission congestion, the authors use fixed transmission rights. A model is introduced that involves a three‐node unidirectional network in order to evaluate the significance of transmission constraints. Data from the PJM, which is located in the eastern USA, were used for model implementation.

The stochastic nonlinear mixed‐integer model presented shows that transmission constraints and fixed transmission rights can have a significant effect on the choices a utility will make when dealing with power procurement. It is demonstrated that the inclusions drastically decrease the value of the objective function.

Conditional value at risk (CVaR) was chosen over VaR as a risk measurement for two different reasons. First, it is important to have a good representation of the trade‐off between the best expected profit and the volatility experienced when obtaining that profit. Second, it provides protection against very undesirable scenarios that may occur with low probability. In order to simplify the fixed transmission rights contracts, a three‐node network is used with unidirectional flow.

When markets were regulated, transmission lines were owned and operated by local utilities, and all power sent over the lines was either owned by the operating utility or wheeled for another utility based on existing agreements. With the advent of deregulation, utilities were forced to wheel other companies' power, which introduced more risk in terms of transmission constraints.

The contribution of this research is to help companies not only hedge the risk of unknown power prices but also unknown transmission congestion. One distinctive feature of the authors' research is to expand upon existing “power portfolio optimization with risk” literature by introducing a transmission constraint into the model. Historically, transmission congestion has been modeled in different ways, including flowgates, transmission rents and fixed transmission rights.

The electric power industry has been moving from a regulated monopoly structure to a deregulated market structure in many countries. The purpose of this study is to comprehensively review the existing markets to study advantages, issues involved and lessons learnt to benefit emerging electricity markets.

The paper employs a comprehensive review of existing competitive electricity market models in USA (California), UK, Australia, Nordic Countries (Norway), and developing country (Chile) to analyze the similarities, differences, weaknesses, and strengths among these markets based on publically available data, literature review and information.

Ongoing or forthcoming electricity sector restructuring activities in some countries can be better designed based on lessons learnt from existing markets and incorporating their own political, technical and economical contexts. A template for design of successful electricity market has also been presented.

This study is limited to a comparative analysis of five markets and can be extended in the future for other existing and emerging electricity markets.

The discussed weaknesses and strengths of existing electricity markets in this study can be practically utilized to improve the electricity industry market structures leading to several social benefits including lower electricity cost.

The comprehensive review and analysis of five existing markets, physically located in different continents, may be used as an assistance or reference guide to benefit the emerging electricity markets in other countries.

Delivering real‐time services (Internet telephony, video conferencing, and streaming media as well as business‐critical data applications) across the Internet requires end‐to‐end quality of service (QoS) guarantees, which requires a hierarchy of contracts. These standardized contracts may be referred to as service level agreements (SLAs). SLAs provide a mechanism for service providers and customers to flexibly specify the service to be delivered. The emergence of bandwidth and service agents, traders, brokers, exchanges and contracts can provide an institutional and business framework to support effective competition. This article identifies issues that must be addressed by SLAs for consumer applications. We introduce a simple taxonomy for classifying SLAs based on the identity of the contracting parties. We conclude by discussing implications for public policy, Internet architecture, and competition.

This paper aims to provide investors’ views on financing costs and barriers to entry into the electricity generation sector, with a focus on investors’ views on potential impacts on cost of capital from adopting nodal pricing and financial transmission rights (FTRs). The implications for policymakers and policy reforms are also discussed in detail.

Survey-based data collection of investors and developers in electricity generation, consisting of multiple choice questions from a closed list of discrete choices, binary-choice questions, and questions with free-text/open-ended answers.

Across survey respondents, weighted-average cost of capital (WACCs) were broadly unchanged over 2019, with increases for undiversified/non-integrated participants offset by decreases for horizontally integrated participants. Cost of equity has risen, whereas cost of debt has fallen. Nodal pricing-cum-FTRs were estimated to increase WACCs by 150–200 basis points p.a. (15–20%), reflecting concerns around the firmness of FTRs and ability to automatically access intraregional settlement residues.

These findings have energy policy implications, namely, the need to consider the interaction between economic theory and real-world financing models when designing and implementing fundamental energy sector reforms.

The need to consider implementation and transitional issues (e.g. grandfathering of existing rights, focusing on reducing the largest barriers to entry) is associated with implementing nodal pricing.

Unique set of survey questions and insights that have not previously been addressed in an Australian context; what-if analysis not previously done in an Australian context

The purpose of this paper is to illustrate how emission constraints imposed by the emission trading scheme (ETS) in the European Union, as well as transmissions capacity, can affect the outcome of the generation scheduling. The aim is to demonstrate the application of the generation scheduling tool which includes both the ETS and transmission constraints, and helps evaluate their effect on emission reduction, costs, and generators' behavior and availability. It can also be used to help generators make strategic decisions regarding utilization and purchases of carbon allowances.

The paper extends the generation scheduling formulation to allow for additional constraints modeling. The formulation is based on the mixed integer programming approach with linearization of generation cost and emission functions, and the possibility to split the system into zones in order to investigate transmission congestion.

The paper presents six case studies that include unconstrained and constrained operation, both from the emission and transmission points of view. It also illustrates the effect of free allocations versus auctioning. The case studies look into the system with wind generation that can be constrained due to transmission limits, and their impact on emission reductions. This is often the case in systems where most of the wind generation is located in the area which does not have sufficiently strong links to the rest of the system where the majority of loads are.

The extension of the work will be inclusion of stochastic nature of emission prices and wind availability. It will also be used for further studies on systems with high wind penetration and insufficient transmission capacity.

The generation scheduling tool and the results from the paper could be useful for generators when making decisions on how to use or purchase their emission allocations, as well as for evaluation of the adverse affect of transmission congestion on carbon emission reductions.

With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.

The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.

Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.

This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.