Although variable renewable energy (VRE) technologies with zero marginal costs decrease electricity prices, the literature is inconclusive about how the resulting shift in the supply curves impacts ...price volatility. Because the flexibility to respond to high peak and low off-peak prices is crucial for demand-response applications and may compensate for the losses of conventional generators caused by lower average prices, there is a need to understand how the penetration of VRE affects volatility. In this paper, we build distributed lag models with Danish and German data to estimate the impact of VRE generation on electricity price volatility. We find that in Denmark wind power decreases the daily volatility of prices by flattening the hourly price profile, but in Germany it increases the volatility because it has a stronger impact on off-peak prices. Our analysis suggests that access to flexible generation capacity and wind power generation patterns contribute to these differing impacts. Meanwhile, solar power decreases price volatility in Germany. By contrast, the weekly volatility of prices increases in both areas due to the intermittency of VRE. Thus, policy measures for facilitating the integration of VRE should be tailored to such region-specific patterns.
•Renewable energy output may increase or decrease electricity price volatility.•Data from Denmark and Germany are used to build distributed lag models.•Explanations for the empirical findings are proposed in order to support policy.
•Renewable portfolio standards (RPS) are market instruments to curb emissions.•In deregulated electricity industries, governments set RPS targets.•Via a bi-level model, we find optimal RPS targets ...with and without market power.•We show that perfect competition leads to a higher RPS target but lower welfare.•Withholding by non-renewable producers alleviates the distortionary effects of RPS.
In order to limit climate change from greenhouse gas emissions, governments have introduced renewable portfolio standards (RPS) to incentivise renewable energy production. While the response of industry to exogenous RPS targets has been addressed in the literature, setting RPS targets from a policymaker’s perspective has remained an open question. Using a bi-level model, we prove that the optimal RPS target for a perfectly competitive electricity industry is higher than that for a benchmark centrally planned one. Allowing for market power by the non-renewable energy sector within a deregulated industry lowers the RPS target vis-à-vis perfect competition. Moreover, to our surprise, social welfare under perfect competition with RPS is lower than that when the non-renewable energy sector exercises market power. In effect, by subsidising renewable energy and taxing the non-renewable sector, RPS represents an economic distortion that over-compensates damage from emissions. Thus, perfect competition with RPS results in “too much” renewable energy output, whereas the market power of the non-renewable energy sector mitigates this distortion, albeit at the cost of lower consumer surplus and higher emissions. Hence, ignoring the interaction between RPS requirements and the market structure could lead to sub-optimal RPS targets and substantial welfare losses.
•We analyze the potential of anticipative planning when undertaking transmission investments.•Anticipating the optimal decisions of the private actors prevents welfare losses.•The socially optimal ...outcome cannot be enforced in a highly uncertain environment.•Investment cost subsidy reduces welfare loss for intermediate values of uncertainty.•Minimum capacity restriction reduces welfare loss in highly uncertain environments.
Private companies (PCs) in restructured electricity industries determine facility investment timing and sizing. Such decisions maximize the PC’s expected profit (rather than social welfare) under uncertainty. By anticipating the PC’s incentives, a welfare-maximizing transmission system operator (TSO) shapes the network to align public and private objectives. Via an option-based approach, we first quantify welfare losses from the PC’s and TSO’s conflicting objectives. We show that by anticipating the optimal timing and capacity decisions of the profit-maximizing PC, the TSO is able to reduce, though not eliminate, welfare loss. Next, we exploit the dependence of the PC’s capacity on the TSO’s infrastructure design to devise a proactive transmission-investment strategy. Hence, we mitigate welfare losses arising from misaligned incentives even in relatively uncertain markets.
•Higher renewable energy penetration requires more flexible generation.•Flexible producers may exploit this leverage to exert market power.•We use a bi-level model to outline strategic offers by such ...a producer.•High intraday prices observed in Nord Pool can be caused by such strategies.•Simultaneous clearing of day-ahead and intraday markets mitigate such outcomes.
The increase in intraday electricity market volumes due to intermittent renewable generation may give a strategic producer an opportunity to exert market power. We study offering strategies of a flexible producer in day-ahead and intraday markets using a bi-level model in which the upper level represents the profit-maximization problem of the producer and the lower-level problems clear the day-ahead and intraday markets sequentially. Using a three-node network, we first demonstrate that a flexible producer with perfect forecasts can increase its profit in both markets by coordinating its offer so as to cause transmission grid congestion or lack of competitive generation capacity. Moreover, we show that strategic behavior is possible even when the day-ahead and intraday markets are cleared simultaneously to lower balancing costs. We next assess these market designs in a Nordic test network and offer an explanation for high Nordic intraday prices. Finally, via an annual simulation using the Nordic market data, we verify that strategic offering in day-ahead and intraday markets under imperfect forecasts leads to increased profits vis-à-vis perfect competition but are mitigated through simultaneous market clearing.
Stochastic adaptive robust optimization is capable of handling short-term uncertainties in demand and variable renewable-energy sources that affect investment in generation and transmission capacity. ...We build on this setting by considering a multi-year investment horizon for finding the optimal plan for generation and transmission capacity expansion while reducing greenhouse gas emissions. In addition, we incorporate multiple hours in power-system operations to capture hydropower operations and flexibility requirements for utilizing variable renewable-energy sources such as wind and solar power. To improve the computational performance of existing exact methods for this problem, we employ Benders decomposition and solve a mixed-integer quadratic programming problem to avoid computationally expensive big-M linearizations. The results for a realistic case study for the Nordic and Baltic region indicate which investments in transmission, wind power, and flexible generation capacity are required for reducing greenhouse gas emissions. Through out-of-sample experiments, we show that the stochastic adaptive robust model leads to lower expected costs than a stochastic programming model under increasingly stringent environmental considerations.
Due to computational constraints, power system planning models are typically unable to incorporate full annual temporal resolution. In order to represent the increased variability induced by large ...amounts of variable renewable energy sources, two methods are investigated to reduce the time dimension: the integral approach (using typical hours based on demand and renewable output) and the representative days method (using typical days to capture annual variability). These two approaches are tested with a benchmark implementation that incorporates full time representation in order identify their suitability for assessing power systems with high renewable penetration. The integral method predicts renewable capacities within a 10% error margin, this paper's main performance metric, using just 32 time steps, while the representative days approach needs 160–200 time steps before providing similarly accurate renewable capacity estimates. Since the integral method generally cannot handle variation management, such as trade and storage, without enhancing the state-space representation, it may be more applicable to one-node models, while the representative days method is suitable for multi-regional models. In order to assess power systems with increasing renewable policy targets, models should be designed to handle at least the 160 time steps needed to provide results that do not systematically overestimate the renewable capacity share.
•Demand-based time-slicing is used to capture variability in power system models.•Proliferation of variable renewable energy sources renders this inadequate.•This paper compares two alternative methods: integral and representative days.•The integral method needs 32 time steps for 10% error margin renewable capacities.•Representative days needs at least 160 time steps to provide a similar accuracy.
•Transmission planners invest in transmission lines in power sectors.•In deregulated industries, policymakers set renewable energy targets.•Perfect competition yields less transmission capacity ...relative to central planning.•A carbon charge on industry maximises welfare only under perfect competition.•However, a carbon charge may actually lower welfare under a Cournot oligopoly.
We explore the role of a transmission system operator (TSO) that builds a transmission line to accommodate renewable energy in order to lower emissions as required by government policy. In contrast to central planning, a TSO in a deregulated electricity industry can only indirectly influence outcomes through its choice of the transmission line capacity. Via a bi-level model, we show that this results in less transmission capacity and with limited emissions control in a perfectly competitive industry vis-à-vis a benchmark centrally planned system. A carbon charge on industry that fully accounts for the cost of pollution damage leads to a perfect alignment of incentives and maximised social welfare only under perfect competition. By contrast, a carbon charge may actually lower social welfare under a Cournot oligopoly as the resulting reduction in consumption facilitates the further exercise of market power.
His bundle pacing is a new method for delivering cardiac resynchronization therapy (CRT).
The authors performed a head-to-head, high-precision, acute crossover comparison between His bundle pacing ...and conventional biventricular CRT, measuring effects on ventricular activation and acute hemodynamic function.
Patients with heart failure and left bundle branch block referred for conventional biventricular CRT were recruited. Using noninvasive epicardial electrocardiographic imaging, the authors identified patients in whom His bundle pacing shortened left ventricular activation time. In these patients, the authors compared the hemodynamic effects of His bundle pacing against biventricular pacing using a high-multiple repeated alternation protocol to minimize the effect of noise, as well as comparing effects on ventricular activation.
In 18 of 23 patients, left ventricular activation time was significantly shortened by His bundle pacing. Seventeen patients had a complete electromechanical dataset. In them, His bundle pacing was more effective at delivering ventricular resynchronization than biventricular pacing: greater reduction in QRS duration (−18.6 ms; 95% confidence interval CI: −31.6 to −5.7 ms; p = 0.007), left ventricular activation time (−26 ms; 95% CI: −41 to −21 ms; p = 0.002), and left ventricular dyssynchrony index (−11.2 ms; 95% CI: −16.8 to −5.6 ms; p < 0.001). His bundle pacing also produced a greater acute hemodynamic response (4.6 mm Hg; 95% CI: 0.2 to 9.1 mm Hg; p = 0.04). The incremental activation time reduction with His bundle pacing over biventricular pacing correlated with the incremental hemodynamic improvement with His bundle pacing over biventricular pacing (R = 0.70; p = 0.04).
His resynchronization delivers better ventricular resynchronization, and greater improvement in hemodynamic parameters, than biventricular pacing.
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Environmental concerns have motivated governments in the European Union and elsewhere to set ambitious targets for generation from renewable energy (RE) technologies and to offer subsidies for their ...adoption along with priority grid access. However, because RE technologies like solar and wind power are intermittent, their penetration places greater strain on existing conventional power plants that need to ramp up more often. In turn, energy storage technologies, e.g., pumped hydro storage or compressed air storage, are proposed to offset the intermittency of RE technologies and to facilitate their integration into the grid. We assess the economic and environmental consequences of storage via a complementarity model of a stylized Western European power system with market power, representation of the transmission grid, and uncertainty in RE output. Although storage helps to reduce congestion and ramping costs, it may actually increase greenhouse gas emissions from conventional power plants in a perfectly competitive setting. Conversely, strategic use of storage by producers renders it less effective at curbing both congestion and ramping costs, while having no net overall impact on emissions.
Lagging public-sector investment in infrastructure and the deregulation of many industries mean that the private sector has to make decisions under multiple sources of uncertainty. We analyze such ...investment decisions by accounting for both multiple sources of uncertainty and the time-to-build aspect. The latter feature arises in the energy and transportation sectors, because investors can decide the rate at which the project is completed. Furthermore, two explicit sources of uncertainty represent the discounted cash inflows and outflows of the completed project. We use a finite-difference scheme to solve numerically the option value and the optimal investment threshold. Somewhat counterintuitively, with a relatively long time to build, a reduction in the growth rate of the discounted operating cost may actually lower the investment threshold. This is contrary to the outcome when the stepwise aspect is ignored in a model with uncertain price and cost. Hence, research and development efforts to enhance emerging technologies may be more relevant for infrastructure projects with long lead times.
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