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This paper analyses the justification of technological choices and options in the context of nuclear energy policy. We argue that “society” increasingly demands a justification with regard to the level of uncertainty and inequality a certain technological choice induces. We aim to demonstrate that policy makers in fact do address these issues, but depending on how they define the problem, this is done in a more explicit (overt) or implicit (covert) way.

First, the changing context with regard to the justification of technological choices is briefly sketched. We draw the attention to the link between the way a certain (energy) policy problem is defined, and the way the framework for political decision‐making is set up in response to the problem. In order to clarify this observation, we make use of a scheme derived from policy sciences, mapping out policy problems in two dimensions: the (lack of) certainty concerning the kinds of knowledge a problem may require, and the (lack of) consensus on relevant values (i.e. “the common good”, “basic rights”, etc.). Each type of policy problem requires a distinct solution strategy. A so‐called Type III‐error occurs when the wrong problem is solved by employing a strategy which does not apply to the problem at hand. In that case, political theory predicts strategic behavior in order to actively suppress or blur value differences. The Belgian decision to phase out nuclear power is used as a case study to illustrate some of the theoretical implications of the scheme.

Several Type III‐errors could be demonstrated in the case of the Belgian phase out. In this case, social learning was severely hampered by different methodological approaches; lack of data; different perceptions of relevant time scales; different framing of the problem; institutional barriers; lack of communication; strategic use of scientific assessments by different stakeholders; and insufficient knowledge of scientific assessments.

The paper aims to broaden the debate on energy policy outside the boundaries of institutional decision‐making. We conclude with some practical recommendations for future energy policy, regarding problem structuring, defining possible options, goal and strategy formulation and monitoring of choices.

With the growing deployment of variable renewable energy sources, such as wind and PV and the increasing interconnection of the power grid, multi-regional energy system models (ESMs) are increasingly challenged by the growth of model complexity. Therefore, the need for developing ESMs, which are realistic but also solvable with acceptable computational resources without losing output accuracy, arises. The purpose of this study is to propose a statistical approach to investigate asynchronous extreme events for different regions and then assess their ability to keep the output accuracy at the level of the full-resolution case.

To extract the extreme events from the residual demands, the paper focuses on analyzing the tail of the residual demand distributions by using statistical approaches. The extreme events then are implemented in an ESM to assess the effect of them in protecting the accuracy of the output compared with the full-resolution output.

The results show that extreme-high and fluctuation events are the most important events to be included in data input to maintain the flexibility output of the model when reducing the resolution. By including these events into the reduced data input, the output's accuracy reaches the level of 99.1% compared to full resolution case, while reducing the execution time by 20 times.

Moreover, including extreme-fluctuation along with extreme-high in the reduced data input helps the ESM to avoid misleading investment in conventional and low-efficient generators.

The purpose of this paper is to present new basis functions suitable to parameterize two‐dimensional static potentials or (magnetic) fields and to show their application in practical cases.

Regular multipole solutions of the potential equation in plane elliptic coordinates are found by separation. The resulting set of functions is reduced to complete subsets suitable for expanding regular potentials or irrotational source‐free fields. Approximate regular plane solutions of the potential equation in local toroidal coordinates are computed by R‐separation and power series expansions in the inverse aspect ratio. The harmonic signals induced in a coil rotating in such a toroidal multipole field are computed from the induction law by similar expansions.

The elliptic expansions are useful in a larger area than circular multipole expansions and give better fits. The toroidal expansions permit one to estimate the effect of the curvature of magnets on the field and give better adapted expansions. However, while the scalar multipoles for the potential are orthogonal the vector fields derived for the two‐dimensional field are not.

Derivations presuppose analytical fields.

Field data obtained from numeric field calculations or measurements do not represent exactly analytic fields. Application of the expansion requires care and checks.

The paper presents novel approaches for parameterizing longitudinally uniform cylindrical or toroidally uniform potentials and fields.

The purpose of this study is to analyze the power market and greenhouse gas (GHG) emission effects of the joint Norwegian–Swedish tradable green certificates (TGCs) market, which is established to support investments according to a 26.4 TWh increased annual renewable electricity generation (REG) by 2020.

The study applies an energy system model with high granularity in time and space, and detailed power system data for the Nordic countries, Germany, The Netherlands and UK.

The results show that the TGC scheme will cause a 8.7-9.3 /MWh reduction in average electricity prices in the Nordic countries. The price decrease will to a limited extent pass through to Germany, The Netherlands and UK. When assuming a low carbon price level, the new REG will reduce annual GHG emissions by 10.9 Mtonnes in 2020, primarily through substitution of German natural gas power. A sensitivity analysis shows that the GHG emission effect of the TGCs is highly sensitive to changes in the carbon price. Investment levels up to a 90 TWh increased REG per year are found to cause increasing GHG emission reductions.

The study results signal the importance of taking the TGC policy into account in decision-making processes in the Northern European power system, in particular for market actors in the Nordic area. The authors conclude that the Nordic countries potentially can play a vital role in a future Northern European low carbon power system through export of green balancing power, substitution of thermal power and reduced GHG emissions from the Northern European power sector.

This paper aims to present two mathematical models to solve the Energy Management problem of a building microgrid (MG). In particular, it proposes a deterministic mixed integer linear programming (MILP) and non-linear programming (NLP) formulations. This paper focuses on the modelling process and the optimization performances for both approaches regarding optimal operation of near-zero energy buildings connected to an electric MG with a 24-h time horizon.

A general architecture of a MG is detailed, involving energy storage systems, distributed generation and a thermal reduced model of the grid-connected building. A continuous non-linear model is detailed along with linearizations for the mixed-integer liner formulation. Multi-physic, non-linear and non-convex phenomena are detailed, such as ventilation and air quality models.

Results show that both approaches are relevant for solving the energy management problem of the building MG.

Introduction and modelling of the thermal loads within the MG. The resulting linear program handles the mutli-objective trade-off between discomfort and the cost of use taking into account air quality criterion. Linearization and modelling of the ventilation system behaviour, which is generally non-linear and non-convex equality constraints, involving air quality model, heat transfer and ventilation power. Comparison of both MILP and NLP methods on a general use case provides a solution that can be interpreted for implementation.

The distributed generation (DG) proper placement is an extremely rebellious concern for attaining their extreme potential profits. This paper aims to propose the application of the communal spider optimization algorithm (CSOA) to the performance model of the wind turbine unit (WTU) and photovoltaic (PV) array locating method. It also involves the power loss reduction and voltage stability improvement of the ring main distribution system (DS).

This paper replicates the efficiency of WTU and PV array enactment models in the placement of DG. The effectiveness of the voltage stability factor considered in computing the voltage stability levels of buses in the DS is studied.

The voltage stability levels are augmented, and total losses are diminished for the taken bus system. The accomplished outcomes exposed the number of PV arrays accompanied by the optimal bus location for various penetration situations.

The optimal placement and sizing of wind- and solar-based DGs are tested on the 15- and 69-test bus system.

Moreover, the projected CSOA algorithm outperforms the PSOA, IAPSOA, BBO, ACO and BSO optimization techniques.

The individual machine learning methods used for fault detection and classification have accuracy performance at a certain level. A combined learning model composed of different base classifiers rather than an individual machine learning model is introduced to ensure diversity. In this way, this study aims to improve the generalization capability of fault detection and classification scheme.

This study presents a probabilistic weighted voting model (PWVM) with multiple learning models for fault detection and classification. The working principle of this study’s proposed model relies on weight selection and per-class possibilities corresponding to predictions of base classifiers. Moreover, it can improve the power of the prediction model and cope with imbalanced class distribution through validation metrics and F-score.

The performance of the proposed PWVM was better than the performance of the individual machine learning methods. Besides, the proposed voting model’s performance was compared with different voting mechanisms involving weighted and unweighted voting models. It can be seen from the results that the presented model is superior to voting mechanisms. The performance results revealed PWVM has a powerful predictive model even in noisy conditions. This study determines the optimal model from among voting models with the prioritization method on data sets partitioned different ratios. The obtained results with statistical analysis verified the validity of the proposed model. Besides, the comparative results from different benchmark data sets verified the effectiveness and robustness of this study’s proposed model.

The contribution of this study is that PWVM is an ensemble model with outstanding generalization capability. To the best of the authors’ knowledge, no study has been performed using a PWVM composed of multiple classifiers to detect no-faulted/faulted cases and classify faulted phases.

The authors formulate India’s energy targets in light of pushing for renewable energy sources and reducing the dependence on imported coal. Share of imported coal in electricity generation has been approximately 10 per cent in recent years. While investments in renewables have grown in recent years as seen in installed capacities, coal-fired electricity generation has grown because of rising demand for electricity. The purpose of this study is to find a planner solution when high global coal prices force greater investments in renewable energies.

The authors use real options approach where global coal prices are the stochastic variable. They present an optimal stopping problem and solving the problem backward, the revenues from continuing with the current energy generation mix and those from replacing imported coal with wind and solar is compared for each period.

The “trigger price” for global coal prices when it is optimal for the social planner to invest in additional wind and solar capacities is found. Trigger prices is the threshold when investment must be undertaken whatever be the future evolution of coal prices; this gives the problem a value of waiting. India cannot afford to wait to invest if faced with strict short-term goals.

The work evaluates India’s domestic targets and its Paris Agreement goals in light of using more of wind and sun and replacing imported coal. Various data sources (government reports, research articles) are consulted to predict shares of electricity from various sources in future and the authors find the operating costs and the investment costs associated with switching to renewables.

With the advent of the information age, this paper aims to apply risk analysis theories to study the risk prevention mechanism of information disclosure, thus supporting the green electricity supply.

This paper conducts a comprehensive evaluation and analysis of the impact of power market transactions, power market operations and effective government supervision, so as to figure out the core risk content of power market information disclosure. Moreover, AHP-entropy method is adopted to weigh different indicators of information disclosure risks for the participants in the electricity market.

The potential reasons for information disclosure risk in the electricity market include insufficient information disclosure, high cost of obtaining information, inaccurate information disclosure, untimely information disclosure and unfairness of information disclosure.

Some suggestions and implications on risk prevention mechanism of information disclosure in the electricity market are provided, so as to ensure the green electricity supply and promote the electricity market reform in China.

This study aims to solve optimal pricing and power bidding strategy problem for integrated combined heat and power (CHP) system by using a modified heuristic optimization algorithm.

In electricity markets, generation companies compete according to their bidding parameters; therefore, optimal pricing and bidding strategy are solved. Recently, CHP units are significantly operated by generation companies to meet power and heat, simultaneously.

For validation, it is shown that profit is improved by 0.04%–48.02% for single and 0.02%–31.30% for double-sided auctions. As heat price curve is extracted, the simulation results show that when CHP system is integrated with other units results in profit increase and emission decrease by 3.04%–3.18% and 2.23%–4.13%, respectively. Also, CHP units significantly affect bidding parameters.

The novelties are pricing and bidding strategy of integrated CHP system is solved; local heat selling is considered in pricing and bidding strategy problem and heat price curve is extracted; the effects of CHP utilization on bidding parameters are investigated; a modified heuristic and deterministic optimization algorithm is presented.