We address the challenges of power flow computation and network operator coordination to implement distribution locational marginal pricing (DLMP) in this paper. Compared with other dynamic pricing schemes, DLMP can give clearer economic signals regarding distributed energy resources (DERs) investment, demand side response, congestion management and network reinforcement. Without neglecting the power loss of distribution network, the second-order cone AC optimal power flow (SOPF) model is used here to calculate DLMP. A distributed economic dispatch mechanism based on the modified Benders decomposition and distributed generation cost (DGC) is proposed to reduce the dispatch complexity in facing high penetration of DERs. The key contribution is that we take the tie-line power flow as the complicating variable to formulate the modified Benders decomposition algorithm. The concept of DGC is proposed to reallocate the global dispatch cost to economically incentivize the regional network operators for coordination. The distributed economic dispatch mechanism is implemented in GAMS grid computing platform. Numerical results show that SOPF can give accurate power flow and DLMP results. The fast convergence of the proposed distributed dispatch is guaranteed by the convexity of the SOPF model and efficient grid computing technique.

The informationally simple approach to incentive regulation applies mechanisms that translate the regulator's objective function into the firm's profit-maximizing objective. These mechanisms come in two forms, one based on subsidies/taxes, the other based on constraints/price caps. In spite of a number of improvements and a good empirical track record simple approaches so far remain imperfect. The current paper comes up with a new proposal, called H-R-G-V, which blends the two traditions and is shown in simulations to apply well to electricity transmission pricing and investment. In particular, it induces immediately optimal pricing/investment but is not based on subsidies. In the transmission application, the H-R-G-V approach is based on a bilevel optimization with the transmission company (Transco) at the top and the independent system operator (ISO) at the bottom level. We show that H-R-G-V, while not perfect, marks an improvement over the other simple mechanisms and a convergence of the two traditions. We suggest ways to deal with remaining practical issues of demand and cost functions changing over time. 

This paper considers hypothetical options for the transformation of the Bolivian power generation system to one that emits less carbon dioxide. Specifically, it evaluates the influence of the weighted average cost of capital (WACC) on marginal abatement cost curves (MACC) when applying carbon taxation to the power sector. The study is illustrated with a bottom-up least-cost optimization model. Projections of key parameters influence the shape of MACCs and the underlying technology configurations. These are reported. Results from our study (and the set of assumptions on which they are based) are country-specific. Nonetheless, the methodology can be replicated to other case studies to provide insights into the role carbon taxes and lowering finance costs might play in reducing emissions.

In this paper, we model strategic interaction of multiple producers in hydrodominated power systems under uncertainty as an equilibrium problem with equilibrium constraints (EPEC), reformulated as a stochastic mixed-integer linear program with disjunctive constraints. We model strategic hydropower producers who can affect the market price by submitting strategic bids in quantity, price, and ramp rate. The bids are submitted to the system operator who minimizes the dispatch cost. We take into account the hydrospecific constraints and uncertainty in the system. Solving the problem results in finding Nash equilibria. We discuss two types of Nash equilibria under uncertainty: Bayesian and robust Nash equilibria. Large EPEC instances can be solved using a decomposition method-Modified Benders Decomposition Approach. This method eliminates the problem of tuning the disjunctive parameter and reduces the memory requirements, resulting in improved computation time.

The energy system in the Nordic countries faces changes driven by increasing integration with the rest of Europe and changes to the generation mix. These developments pose challenges with respect to future network development and operation. We focus on three major aspects: market integration; generation and network adequacy; the need for more flexibility and frequency control. We describe factors behind these problems and present possible solutions within the Nordic context. One conclusion is that supranational cooperation should be further improved.

This paper proposes a mathematical model for transmission investment game where there are several transmission planners (TPs). The model is developed assuming a simultaneous-move game between TPs. Each TP maximizes the total surplus (producers', consumers', and transmission surplus) minus the investment cost of its region given the investment decisions of rival TPs. The transmission investment risk is also considered using the probability-of-shortfall measure. We assume one market operator who dispatches the generators in all TP's regions. The risk-constrained Nash equilibria model is formulated as amixed-integer linear program (MILP). To solve the proposed MILP, a solution algorithm is proposed that combines the standard branch-and bound algorithm (BB) with a proposed modified benders decomposition algorithm (MBD). The proposed BB-MBD algorithm is also parallelized to improve the computation performance. To improve the coordination between TPs, a supporting budget mechanism is also mathematically modeled in the MILP. The numerical results are carried out using the 9-bus 3-area and the IEEE Three Area RTS-96 networks. The computational performance of proposed BB-MBD is compared with standard BB algorithm.

This paper proposes a probabilistic pricing which achieves efficient operation of and investment in ramp-service providers in power systems with a large amount of wind or solar generation. The proposed pricing differs from the existing literature in that it focuses exclusively on the efficient dispatch of electrical energy with no exogenous consideration of the need for reserves or balancing services. The proposed optimal dispatch task determines both the efficient level of any preventive actions taken before a contingency event occurs and the efficient response of the power system - i.e., corrective actions - once an event occurs. We show analytically that the efficient dispatch outcome can be achieved in a decentralized market mechanism provided the market participants are profit-maximizers and price-takers. We show how the total economic benefit of an investment can be decomposed into two components (a) the normal dispatch cost benefit and (b) the economic value of the investment in contributing ramp service to the power system. In order to study different aspects of the probabilistic pricing, the IEEE 30-node example system is deliberately modified. The results show the efficiency of the proposed pricing and the use of the investment model to assess the economic value of ramp-service providers.

This paper proposes a stochastic bilevel program for strategic bidding of a hydropower producer. The price, quantity and ramp-rate bids are considered. The uncertainty of wind power generation, variation of inflows for the hydropower producer, and demand variability are modeled through the moment-matching scenario generation technique. Using discretization the stochastic bilevel program is reformulated as a stochastic mixed-integer linear program (MILP) with disjunctive constraints. We propose a modified Benders decomposition algorithm (MBDA), which fully exploits the disjunctive structure of reformatted MILP model. More importantly, the MBDA does not require optimal tuning of disjunctive parameters and it can be efficiently parallelized. Through an illustrative 5-node example, we identify possible strategies (specific to a hydropower producer) for maximizing profit, which in turn leads to market insights. We also use the IEEE 24-node, 118-node, and 300-node case studies to show how our parallelized MBDA outperforms the standard benders decomposition algorithm. The parallelized MBDA is also compared to the state-of-the-art CPLEX solver.

Transmission charges aim to recover the cost of transmission network investments and provide efficient locational signals to new generators. In this paper, we investigate the effect of these charges on the development of new generation capacities in the system. Generation expansion planning is decided by strategic generation planners (SGPs) trying to maximize their profits, while transmission line investments are planned by a central planner and regulatory body aimed at minimizing the overall operation and network investment costs of the system. Regulatory transmission charges (RTCs) are calculated according to the marginal responsibility of generation investment on transmission network investment costs. An iterative algorithm is proposed to model the interaction taking place between the central planner and SGPs. The developed methodology is applied to a 2-node illustrative example and the IEEE-RTS96, and effects of RTCs on investment decisions of SGPs are analyzed.